1
2
Original Research Article
4
Pupillometry via smartphone for low-resource
5Q1
settings
6 7
8
Davide Piaggio
a,*, Georgy Namm
a, Paolo Melillo
b, Francesca Simonelli
b,
9Q2Ernesto Iadanza
c, Leandro Pecchia
a10 aApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV47AL,
11 United Kingdom
12 bEye Clinic, Multidisciplinary Department of Medical, Surgical and Dental Sciences, Universita` degli Studi della Campania ‘Luigi
13 Vanvitelli’, Naples, Italy
14 cDepartment of Information Engineering, University of Florence, Florence, Italy
15
16
1 8 A R T I C L E I N F O
19 Article history:
20 Received 8 December 2020
21 Received in revised form
22 5 May 2021 23 Accepted 28 May 2021 24 Available online xxxx 25 Keywords: 26 Clinical engineering 27 Low-resource settings 28 Pupillometer 29 mHealth 30 Contextual Design 31 Frugal Engineering 32 3 3 A B S T R A C T
34 The photopupillary reflex regulates the pupil reaction to changing light conditions. Being
35 controlled by the autonomic nervous system, it is a proxy for brain trauma and for the
con-36 ditions of patients in critical care. A prompt evaluation of brain traumas can save lives.
37 With a simple penlight, skilled clinicians can do that, whereas less specialized ones have
38 to resort to a digital pupilometer. However, many low-income countries lack both
special-39 ized clinicians and digital pupilometers.
40 This paper presents the early results of our study aiming at designing, prototyping and
41 validating an app for testing the photopupillary reflex via Android, following the European
42 Medical Device Regulation and relevant standards.
43 After a manual validation, the prototype underwent a technical validation against a
com-44 mercial Infrared pupilometer. As a result, the proposed app performed as well as the
man-45 ual measurements and better than the commercial solution, with lower errors, higher and
46 significant correlations, and significantly better Bland-Altman plots for all the
47 pupillometry-related measures.
48 The design of this medical device was performed based on our expertise in low-resource
49 settings. This kind of environments imposes more stringent design criteria due to
contex-50 tual challenges, including the lack of specialized clinicians, funds, spare parts and
consum-51 ables, poor maintenance, and harsh environmental conditions, which may hinder the safe
52 operationalization of medical devices. This paper provides an overview of how these
53 unique contextual characteristics are cascaded into the design of an app in order to
con-54 tribute to the Sustainable Development Goal 3 of the World Health Organization: Good
55 health and well-being.
https://doi.org/10.1016/j.bbe.2021.05.012
0168-8227/Ó 2021 The Author(s). Published by Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Abbreviations: IFMBE, International Federation of Medical and Biological Engineering; IR, InfraRed; LMIC, Low- and Middle-Income Country; LRS, Low-Resource Setting; MAE, Mean Absolute Error; RMSE, Root Mean Square Error; SSA, Sub-Saharan Africa; SDG, Sustainable Development Goals; UN, United Nations; WHO, World Health Organization
* Corresponding author at: Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV47AL, United Kingdom.
E-mail address:d.piaggio@warwick.ac.uk(D. Piaggio).
A v a i l a b l e a t
w w w . s c i e n c e d i r e c t . c o m
ScienceDirect
56 Ó 2021 The Author(s). Published by Elsevier B.V. on behalf of Nalecz Institute of
Biocyber-57 netics and Biomedical Engineering of the Polish Academy of Sciences. This is an open access
58 article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
59 60
61
62
1.
Background
63 The photopupillary reflex regulates the pupil dilation and
64 constriction according to the intensity of the light that hits
65 the retina and is controlled by the sympathetic and
parasym-66 pathetic nervous systems. Therefore, this reflex is used as an
67 indirect measure of the central and autonomic nervous
sys-68 tem[1]. The key medical applications of the photopupillary
69 reflex measurements include the detection of brain trauma
70 and the assessment of its severity[2,3], the assessment of
71 the level of anesthesia and pain[4,5], an aid to the
certifica-72 tion of death[6], the evaluation of alcohol[7]and drug
intox-73 ication[8,9], and the study of ophthalmological diseases such
74 as diabetic retinopathy and Horner’s syndrome[1,10]. A quick
75 evaluation of brain trauma via pupillometry, i.e., the
mea-76 surement of pupil size, symmetry and reactivity, can make
77 the difference on the patient’s health and future life and is
78 an essential part of the supportive care provided in this case
79 [11,12]. The early management of traumatic brain injury, in
80 fact, minimizes the progression of the injury and improves
81 recovery and clinical outcomes[12,13]. Accordingly, in many
82 high-income countries, technologies for photopupillary reflex
83 analysis have been proposed[14–17], also using smartphones
84 [18–21]. Recently, an app for tracking the photopupillary reflex
85 using trained object-detectors was introduced[22]. As regards
86 the pupil and iris detection algorithms, there are various
tech-87 nical solutions available including edge detection and Hough
88 transform[23], Starburst transform[24], blob detection
algo-89 rithms[25], watershed segmentation[26], gradient vector flow
90 snake-based method[27], and deep learning[28].
91 However, very little has been proposed for low- and
92 middle-income countries (LMICs), where traumatic brain
93 injury is becoming one of the main causes of morbidity and
94 mortality. In fact, Africa owns less than 5% of the motor
vehi-95 cles in the world and accounts for 10% of global deaths caused
96 by vehicular injuries[29]. In LMICs, and in particular in
low-97 resource settings (LRSs), there is a lack of expertise and
diag-98 nostics to assess brain trauma[30]. Accordingly, the United
99 Nations (UN) aims to ‘‘halve the number of global deaths
100 and injuries from road traffic accidents”. This is target 3.6 of
101 the UN Sustainable Development Goals (SDGs) number 3,
102 Good health and well-being[31].
103 The photopupillary reflex can be measured with a simple
104 penlight. Despite the simplicity of the device, accurate and
105 reliable assessments of the photopupillary reflex require an
106 experienced user: Couret et al. [32]demonstrated that the
107 penlight photopupillary reflex observation in neurocritical
108 care is prone to human error, limited reproducibility and
109 low precision. In many LMICs, diagnosis and healthcare
deliv-110 ery is hindered by the lack of specialized clinicians, alongside
111 the lack of resources and poor supply chain[33]. An
alterna-112
tive is the digital pupillometer, i.e., a medical device
perform-113
ing automated pupillometry using infrared cameras, which
114
are expensive and not designed (i.e., not resilient) to operate
115
in the harsh environments (i.e., dusty, warm, humid, with
116
unstable power supply etc.) typical of Sub-Saharan Africa
117
(SSA).
118
This article presents the early results of our study aimed at
119
designing, prototyping and validating a mobile app, based on
120
relevant international and military standards, for testing the
121
photopupillary reflex via Android in LRSs.
122
The aim of this app is to act as a screening tool that can be
123
used by nurses (or also lay-users) to test the direct pupillary
124
reflex in order to screen the incoming patients’ conditions
125
(e.g., suspected presence of brain injuries) and plan further
126
investigations. This is crucial in LRSs.
127
Specifically, this paper describes the acquisition of videos,
128
the signal processing and their technical validation. The
129
results from eight field studies in SSA have informed the
con-130
textualized and user-driven design, and can also be relevant
131
for informing the design of other devices for LMICs. In fact,
132
we added additional design criteria, due to the challenges
133
typical of SSA, which included the lack of specialized
clini-134
cians, the scarcity of funds, of spare parts and consumables,
135
poor maintenance, which hinder the safe and efficient
opera-136
tionalization of medical devices. This paper demonstrates
137
how these peculiar contextual characteristics can be
cas-138
caded into the design of a mobile app and redesign of a
med-139
ical device.
140
This work was inspired and informed by existent
regula-141
tions and standards. In particular, those related to existing
142
pupillometers were taken into consideration, because of their
143
similarity to our solution. Further punctual analysis of
stan-144
dards and requirements will be needed in the later stages to
145
pass from prototype to product.
146
2.
Methods
147
2.1. Ethnography-driven user-need and contextual
148 analysis in LMICs
149
Designing medical devices for LRSs requires the synergy of
150
different but complementary methodologies, comprising of
151
not only engineering, scientific and quantitative techniques,
152
but also qualitative approaches such as ethnography research
153
[34]. Ethnography applied to the design is, in fact, one of the
154
keys to further develop the current technological progress,
155
by allowing designers and researchers to understand the
156
design challenges more deeply, with a focus on a particular
157
kind of end-users and their surrounding contexts. For this
158
reason, we conducted the need and context analyses with a
159
160 They were structured in three steps, which were iterated
161 twice: general formalization, contextualization in SSA
coun-162 tries, and field studies in Benin and Uganda.
163 The first requirements were identified by reviewing the
lit-164 erature on medical devices and their related standards (i.e.,
165 ISO 14971 – Medical devices – Application of risk management
166 to medical devices, IEC 62366 – Medical devices—Part 1:
Appli-167 cation of usability engineering to medical devices, IEC 62304 –
168 Medical device software—Software life cycle processes, ISO
169 15004 – Ophthalmic instruments—Fundamental
require-170 ments and test methods), and performing focus groups with
171 international experts of medical device design and
manage-172 ment, and hospital engineering. Five focus groups were held
173 with world leading experts of biomedical and clinical
engi-174 neering during international conferences[35–39].
175 The contextualization in SSA was performed by: 1)
admin-176 istering surveys to African Scholars; 2) holding focus groups
177 with biomedical and clinical engineers in SSA countries (in
178 accordance with the ethical approval REGO-2018-2283). Five
179 focus groups were held in SSA and were attended by delegates
180 from more than 12 SSA countries (two during the
Africa-181 Health conferences, two in Benin at the Ecole Polytechnique
182 d’Abomey-Calavi, and one in Ethiopia)[40].
183 Three field studies were conducted in Benin in April 2017,
184 January 2018 and November 2019 and one in Uganda in
Octo-185 ber 2019. During these studies, several aspects of medical
186 devices and medical locations were analyzed. This included
187 electric measurements, examinations of medical devices,
188 inspections of medical locations in 6 African hospitals and
189 semi-structured interviews with the available staff, including
190
biomedical engineers, technicians, nurses, doctors and
hospi-191
tal directors[41–43].
192
In collaboration with the International Federation of
Med-193
ical and Biological Engineering (IFMBE) African Working
194
Group[40], focus groups were organized.
195
The ethnographic analysis was conducted in accordance
196
with the ethical approval REGO-2018-2283, obtained from
197
the Biomedical and Scientific Research Ethics Committee.
198
For quality insurance, we followed the prescriptions of the
199
European regulations on medical devices, which equate
med-200
ical apps to medical devices. Moreover, we based our work on
201
the 5As principles of the World Health Organization (WHO),
202
i.e., affordability, availability, adequacy, accessibility, and
203
appropriateness, in line with the solutions proposed in the
204
WHO compendium of innovative health technologies for
205
LRSs.
206
2.2. Development of the smartphone-based pupillometer
207
The development of the smartphone-based pupillometer
fol-208
lowed 5 stages, namely the smartphone pupil stimulation and
209
video acquisition, the preprocessing, the image processing,
210
the system integration and the technical validation, which
211
will be described thoroughly in the following subsections.
212
These stages were developed and validated with videos
213
acquired from 11 healthy subjects in accordance with the
eth-214
ical approval obtained from the Ethical Committee of
Univer-215
sity of Campania Luigi Vanvitelli1. Further details about the
216
dataset can be found elsewhere[14].
217 2.2.1. Smartphone pupil stimulation and video acquisition
218
During the first feasibility study, the pupil of a subject with
219
light brown eyes was stimulated with the flash embedded in
220
a smartphone (i.e., ZTE Blade C341), with the illuminance
221
set at 480 lx and the duration at 500 ms. The photopupillary
222
reflex was captured with a second smartphone, namely a
223
Samsung Galaxy a7 (2016) with a 13-megapixel camera,
224
although the final app integrates both functions and only
225
one smartphone without tripod support is needed for future
226
use. In fact, in the final app the video recording and the flash
227
are synchronized as follows: the flash starts 2 s after the
228
recording has started, lasts for 500 ms and the recording is
229
stopped when 9 s in total are reached. This allows a recording
230
of about 6–7 s of the pupil reaction, in line with the typical
231
duration of the event[44].
232
Both smartphones were selected depending on their
avail-233
ability at the times of the experiments and were placed on a
234
tripod at a distance of 8 cm from the subject’s face. In
litera-235
ture, distances in the range of 8–15 cm were used[20,45,46].
236
As the luminance will vary according to the distance from
237
the light source following an inverse-square law [47], the
238
luminance at various distances (i.e., 5–20 cm, with a 1-cm
239
step) was also evaluated to check whether small differences
240
in distance could significantly affect it. It resulted that in
241
the above-mentioned range, i.e., 11.5 ± 3.5 cm, the luminance
242
had an average percentage change of 10.7%. Furthermore, we Fig. 1 – Context-driven design: the methodologies used for
the design and evaluation of the proposed pupillometer.
1Registration number 500, approval title ‘‘Studio pilota sull’utilizzo della pupillometria cromatica per la diagnosi e il monitoraggio delle degenerazioni retiniche ereditarie”).
243 investigated whether the flashes at 8 cm and at 15 cm would
244 trigger a pupil reaction, and whether the minimum reached
245 by the pupil would be similar. This was tested with two
246 smartphone models available at the time of writing (i.e., a
247 Doogee S60 Lite and an iPhone 7). The pupil minimum,
248 expressed as the normalized pupil/iris ratio, resulted to be
249 varying in the range 71.2 ± 2.7% (percentage variation of
250 3.8%) with the Doogee S60 Lite flash, and in the range 50.7
251 ± 0.4 (percentage variation of 0.8%) with the iPhone 7. This
dif-252 ference is probably due to the more powerful flash embedded
253 in the second smartphone model. Therefore, assuming the
254 test will be run with the same device, the pupil contraction
255 will remain substantially the same in the recommended
dis-256 tance range.
257 The height at which the two smartphones were placed was
258 so that the eye resulted to be at the center of the frame. The
259 opposite eye was neither stimulated by the flash, nor covered.
260 Throughout these experiments the ambient light was
mea-261 sured using a luxometer (Dr. Meter, LX1010B), in order to
262 ensure an approximately constant baseline light intensity.
263 The videos were then fed to a dedicated algorithm as
264 detailed below. At this prototyping stage all the signal
elabo-265 rations are performed on Matlab.
266 In the future, the definitive algorithm will either run on a
267 dedicated server application that will include a Matlab
com-268 piled dynamic link library (DLL) to foster the accessibility of
269 older models of smartphones, or will be embedded in the
270 mobile device itself, for the most performing models.
271 2.2.2. Preprocessing
272 The images contained in the frames of the video are
prepro-273 cessed by turning them into gray-scale (seeFig. 2, IIa),
binariz-274 ing them according to a certain threshold, and going through
275 morphological opening (i.e., erosion followed by dilation) and
276 closing (i.e., dilation followed by erosion) to remove any dark
277 unrelated pixel or particularly small objects (seeFig. 2, IIb).
278
The binarization phase is a pivotal pre-step and can be
influ-279
enced by the overall light intensity of the frames: if the
inten-280
sity changes over the frames, the results may not be ideal. In
281
case the acquisition is performed with high intensity of light
282
(e.g., flashlight on), a higher threshold is selected, by
assess-283
ing the mean intensity of the first 3 frames (i.e., baseline)
284
and setting the highest threshold if the mean intensity of a
285
frame results 5% greater than the baseline. The values of
286
the two thresholds were determined empirically. The final
287
app records 9-s H.264 encoded-videos with a 30-fps framerate
288
and an average size of 11.5 MB.
289 2.2.3. Image processing
290
The image recognition algorithm consists of two main parts:
291
the pupil and the iris recognition. The reason behind the
292
choice of including the iris part is due to the contextualization
293
of the design and will be further explained in the results
sub-294 section 3.1named ‘‘Ethnography-driven user-need and
con-295
textual analysis”.
296
2.2.3.1. Pupil recognition algorithm. The algorithm (see
297 Fig. 2) starts by prompting a user input, i.e., the framing of
298
the part of interest (i.e., the eye). The user can draw a
rectan-299
gular box, superimposing it on the first frame of the video,
300
and the coordinates of such polygon will be utilized to crop
301
the frames used during the tuning step of the algorithm
302
(seeFig. 2, I). The latter consists in running the commands
303
for preparing the images (as described above) and for finding
304
the pupil and its center, only over the first three frames of the
305
video.
306
As regards the pupil recognition, three different
307
approaches were tested, namely blob-detection algorithm,
308
circular Hough transform, and watershed transform. These
309
methods were assessed computing the mean absolute error
310
(MAE) and the correlation with the manual measurement
311
(Pearson’s r). In particular, the blob-detection algorithm used
Fig. 2 – The flowchart illustrates the various steps of the preprocessing and image processing, with a specific focus on the recognition of the pupil.
312 in the comparison was a variation of the original that
con-313 sisted in removing the spikes and substituting them with
314 the average value of the points preceding and following the
315 spike.
316 The blob-detection algorithm was inspired by Barragan’s
317 [25]algorithm, which detects the ‘‘blob” with the greatest area
318 contained in the picture, circles it, and finds its center. The
319 diameter output of the tuning stage is called baseline
diame-320 ter, because it is then utilized to automatically calculate the
321 cropping frame dimensions for the main part of the
algo-322 rithm, by creating a framing square with a side equal to four
323 times the baseline diameter, centered on the pupil. The same
324 pupil recognition algorithm is then called again upon all the
325 newly-cropped frames and an array containing the
unpro-326 cessed diameter is saved (seeFig. 2, III).
327 2.2.3.2. Iris recognition algorithm. The algorithm (seeFig. 3)
328 starts by prompting a user input, i.e., the framing of the part
329 of interest (i.e., the iris). A circle is superimposed over the first
330 frame and the user can resize it according to the iris boundary
331 in the frame. Given the position of three points of such circle,
332 its equation is derived as well as the baseline radius of the
333 iris, which is then used as a parameter for the circular Hough
334 transform algorithm (seeFig. 3, II).
335 In this case, the same three algorithms tried out for the
336 pupil recognition were tested as well.
337 Before being fed to this algorithm, the frames are
prepro-338 cessed as described above (seeFig. 3, I, IIIa, IIIb). Moreover,
339 as the outcome of the application of the circular Hough
trans-340 form algorithm depends on the sensitivity, an extra
precau-341 tion is taken in this direction. In fact, although the initial
342 sensitivity is set to 0.88, if no circle is found during the first
343 run, the sensitivity is increased by 0.02 until a circle is found.
344 An array containing the unprocessed diameter is saved.
345
2.2.3.3. Postprocessing. The acquired pupil measurements
346
were often subject to artifacts such as blinking and image
347
overexposure due to flash. For this reason, a series of
func-348
tions were applied in order to smooth spikes and filter out
349
noise. In particular, the affected data were identified,
350
removed from the dataset and the gaps were filled using an
351
interpolation.
352
As regards the flash, the above-mentioned threshold for
353
the binarization was designed to tackle this problem.
How-354
ever, the method is not completely robust and can fail to
355
switch to the correct threshold in the first and final frames
356
of the flash, when it is not at full brightness. Consequently,
357
the flash-related frames were removed and substituted with
358
a linear interpolation. This would not affect the overall
perfor-359
mance as the first part of the constriction phase of the
pho-360
topupillary reaction is steep and approximately linear.
361
As regards the blinking artifact, it partially or completely
362
obstructs the pupil, making the algorithm track nothing or a
363
larger area (e.g., a shadow under the eyelid). Such sudden
364
change of the detected area is a good indicator of when the
365
pupil detection fails. Detrending and differentiation were
366
used to identify these sudden changes: the data points
367
affected by blinking are flagged when the local derivative
368
exceeds a threshold in magnitude. Also in this case, the
369
flagged points are removed and linear interpolation is used
370
to fill the gaps.
371
Finally, the ratio between the diameter of the pupil and
372
that of the iris is calculated and normalized to the initial
373
value. The values related to the frames preceding the pupil
374
reaction are individuated and substituted with 100% values.
375
The part of the array related to the pupil reaction is fitted with
376
a Gamma function, as suggested by Knapen et al. [48](see
377 Fig. 2, IV).
378 The algorithm also calculates some variables relevant to
379 pupillometry:
380
381 Pupil minimum, the minimum size reached by the pupil at
382 the end of the constriction phase; the constriction phase
383 was considered to start when the pupil/iris normalized
384 ratio fell under 98% of its original value;
385
386 Latency, the delay in the pupil response calculated as the
387 time between the start of the flash and the start of the
con-388 striction phase;
389
390 Max constriction velocity, the maximum rate of change in
391 the pupil diameter during the constriction phase;
392
393 Mean constriction velocity, the average rate of change of
394 the pupil diameter during the constriction phase;
395
396 Mean dilation velocity, the average rate of change of the
397 pupil during the dilation phase, which is contiguous to
398 the constriction phase and was considered to end when
399 the pupil/iris normalized ratio overtook 98% of its original
400 value;
401
402 T75, the time implied by the pupil to recover 75% of the
403 amplitude of the constriction starting from the peak of
404 the constriction;
405
406 2.2.4. System integration
407 The resulting pupillometry system has been designed as a
408 three-tier application:
409
410 Presentation layer: the Android app will be used for
acquir-411 ing the video samples and firing the flash.
412
413 Logic layer: a connection with the server code performing
414 the analyses will be developed. The app will act as client.
415 In case of high performance devices, both the client and
416 the server software will be running on the mobile device.
417
418 Data layer: our system will be linked to the database and
419 web application described in[15–7]through RESTful dialog
420 and dedicated APIs.
421
422 2.2.5. App design
423 The app was developed in Android Studio, using Java for the
424 implementation of functions and XML for the design of the
425 user interface, targeting Android-based smartphones with
426 an API level of at least 21 (i.e., Android 5.0 Lollipop), because
427 of the use of the ‘‘camera200 package. This choice allows
428 94.1% of Android users to use our app, as only 5.9% of the
429 Android-based smartphones have an API level lower than 21
430 worldwide (and similar trends can be found in Africa) (from
431 Android Platform/API version distribution – Android Studio)
432 [49].
433
The logo, representing an eye-shaped logarithmic spiral,
434
was hand-drawn and digitized using GIMP (GNU Image
435
Manipulation Program).
436
2.3. Technical validation
437 2.3.1. Video acquisition procedure and image processing
438
All the frames of one of the acquired videos were analyzed
439
both with the Matlab algorithm and manual measurements.
440
In particular, the frames were analyzed manually by two
441
independent authors that were blinded to the output of the
442
pupillometer, in order to reduce the risk of bias: for each
443
frame the diameters of the pupil and of the iris were
mea-444
sured twice and averaged in order to reduce the measurement
445
error.
446
Also in this case, the values related to the frames
preced-447
ing the flash were individuated and substituted with 100%
448
values. Consequently, Pearson’s r and the associated
p-449
value, the root mean square error (RMSE) and the MAE were
450
calculated for the Gamma-fitted signal and the raw
auto-451
mated signal, compared to the manual measurements.
More-452
over, the error rate was estimated by calculating the percent
453
error and counting how frequently it would go over a 10%
454
threshold.
455
2.3.2. Benchmarking
456
Our pupil tracking algorithm was also validated against the
457
output of an IR Pupillometer (DP-2000 – NeurOptics). The
458
gamma fit in this case was not needed because of the
non-459
interaction between the flash and the IR recording. The
tech-460
nical validation was done based on the output variables,
spec-461
ified above. In particular, the variables outputted by the IR
462
pupillometer were normalized in respect to the initially
mea-463
sured pupil size in order to make them comparable with those
464
resulting from our algorithm.
465
The RMSE and MAE were calculated for each variable and
466
for both the algorithms, comparing them with those coming
467
from the manual measurements, taken by two independent
468
and blinded authors. Consequently, 3 Bland-Altman plots
469
were generated for each of the 4 variables, after testing
470
whether their residuals were normally distributed with a
471
Shapiro-Wilk test[50](normality being a necessary condition
472
for such plots). The Bland-Altman plots compared the app
473
Algorithm and the IR Algorithm, the app algorithm and the
474
Manual measurements and the IR algorithm and the Manual
475
measurements.
476 2.3.3. Testing the safety of the flash
477
An experiment was set up to test the safety of use of a
smart-478
phone flash on the human eye, although the safety of the
pro-479
cedure has been confirmed by preliminary research[45]. The
480
smartphone was placed on a tripod and an operator held the
481
sensor of a luxometer (Dr. Meter, LX1010B) in front of the
482
camera at a distance of interest for pupillometry, i.e.,
483
8.5 cm. Firstly, the illuminance in this condition (i.e., ambient
484
light) was recorded; secondly the flash was turned on and the
485
illuminance in this condition was recorded. Hence, the
illumi-486
nance could be easily calculated and compared against the
487
488
3.
Results
489 3.1. Ethnography-driven user-need and contextual
490 analysis in LMICs
491 The contextual analysis highlighted that SSA countries have
492 [41,43]: extremely limited resources, an insufficient number
493 of healthcare professionals and of specialized doctors;
inade-494 quate hospital infrastructures, highly unstable main power
495 supply, poor transport infrastructure and supply-chain, and
496 an uneven distribution of the resources that are concentrated
497 in the capital to the detriment of remote areas. Nonetheless,
498 SSA can count on a very young population, a wide diffusion of
499 mobile phones, smartphones[52], and ICT literacy. There is a
500 wide diffusion of one dominant smartphone operative system
501 (i.e., 86.39% of smartphones based on Android)[53]and a good
502 coverage of wireless telecommunication. Prospectively, the
503 SSA market of medical devices is fast growing (the compound
504 annual growth rate is around 6%)[54]. The adoption of new
505 technologies meets limited inertia and healthcare operators
506 are resilient. In fact, working in challenging conditions
507 pushes workers to practice with the unpredictable conditions
508 and events, developing a great capability to react to, respond
509 to and recover from emergencies. Nonetheless, this positive
510 attitude comes with evident risks too. Often,
non-511 specialized personnel respond to the medical devices
mal-512 functioning with creative shortcuts, which tend to become
513 chronic solutions, prone to new risks, hindering the recovery
514 of the initial level of effectiveness and safety[55]. Finally, a
515 massive ‘‘brain drain” affects doctors and specialized doctors,
516 who move to other countries for better opportunities, further
517 depleting SSA health care systems[56].
518 The results of the contextual analysis have been discussed
519 with African scholars and healthcare personnel in Benin,
520 Ethiopia and South Africa, resulting in a series of
specifica-521 tions for the local manufacturing of a resilient pupillometer,
522 with its consumables and spare parts. The design should be
523 low-cost, based on free design and manufacturing processes,
524 it should empower non-specialized healthcare personnel and
525 providing clear guidance or affordances, possibly be
battery-526 based and resilient to the unstable power supply, resilient to
527 misuses, requiring no maintenance and easy to clean, and
528 based on Android smartphones, possibly compatible with
529 the high degrees of ingress protection (e.g., IP68) described
530 in IEC 60,529 and with rugged and military standards (e.g.,
531 MIL-STD-810G).
532 None of the pupillometers reviewed resulted sufficiently
533 resilient to LMICs. Existing smartphone solutions meet the
534 cost-requirement, but as it emerged from our study, this is
535 not the only criterion for being resilient in LMICs. For
exam-536 ple, most of the proposed solutions widely utilized
acces-537 sories and spare parts, including external LEDs, filters, and
538 lenses, which will hinder the lifetime of the device in SSA.
539 In fact, such parts would be difficult to retrieve, repair or
540 replace in LRSs[41,43].
541 Moreover, when deepening the design principles of a
542 pupillometer, two technical requirements emerged:
computa-543 tional capability compatible with an old Android smartphone;
544
use no accessories or only accessories that could be locally
545
manufactured (e.g., 3D printed).
546
This last criterion particularly influenced the design of the
547
app. The majority of existing pupillometers utilize visible
548
light to stimulate the pupil and infrared (IR) cameras to film
549
its constriction, in order to avoid artefacts. Most smartphones
550
do not contain IR cameras, therefore visible light was used
551
both to stimulate the pupil, using the phone flash, and to film
552
its reaction with the phone camera. As a consequence, the
553
video frames coinciding with the flash resulted overexposed
554
due to the sudden change of luminosity and the proximity
555
of the subject, requiring the adoption of a fitting algorithm
556
to recover the missing pupil diameter in those frames.
More-557
over, phone camera framerates are lower than the one of
558
many pupillometers. Thus, the proposed algorithm fitted
559
the acquired diameter data first with a linear fitting, in order
560
to recover missing data due to the flash, and then with a
561
Gamma distribution for approximating missing frames,
562
reconstructing the complete response of the pupil, as
pro-563
posed in[48]. The interpolation also reduced the blinking
arti-564
facts, affecting also standard pupillometry. Moreover, the
565
distance between the eye and the device created artifacts in
566
the estimation of the pupil diameter. These artifacts could
567
be limited with a recycled plastic 3D printed accessory clipped
568
on the mobile phone, aiming at keeping the eye to phone
dis-569
tance constant. However, since a 3D printer could be not
570
available, the proposed algorithm for the recognition of the
571
pupil reflex was based on the ratio between the diameter of
572
the pupil and that of the iris. In fact, while the pupil diameter
573
reacts to light, the iris does not. The ratio was normalized
574
with the value measured before the flash shooting to facilitate
575
the reading of the pupil diameter. The adoption of these
fea-576
tures required a specific technical validation of the final
algo-577
rithm and app.
578
3.2. Development of the smartphone-based pupillometer
579
A total of 4 videos were recorded, in which the eye was
stim-580
ulated 3 times in order to be sure to capture a good-quality
581
response (i.e., absence or reduced number of blinks).
582 3.2.1. Preprocessing and image processing
583
Three methods were tested for the pupil and iris detection: a
584
blob-detection algorithm, the circular Hough transform, and
585
the watershed transform. The blob-detection algorithm
out-586
performed the other methods with lower MAE (3.9% versus
587
4.55% of the Hough transform, and 21.25% of the Watershed
588
transform) and higher correlation (Pearson’s r of 0.95 and
p-589
value <0.00001 versus 0.84 and p-value p-value <0.00001 of
590
the Hough transform, and0.03 and p-value of 0.83), being
591
selected for the pupil tracking (Fig. 2, III). This choice also
592
avoided the introduction of an extra user input, i.e., the pupil
593
radius range, which is necessary for the Hough transform to
594
work. The Hough transform outperformed the other methods
595
in tracking the iris. Consequently, the Hough transform was
596
performed for the iris tracking (see Fig. 3, IV). Fig. 4shows
597
the comparison of three signals, namely the gamma-fitted
598
599 removal of the flash and blink artifacts via post-processing
600 are evident inFig. 4.
601 3.2.1.1. App design. The app, named Oida (meaning ‘‘I
602 have seen” and ‘‘I know”, from Ancient Greek ‘‘ὁqάx”), for
603 tracking the photopupillary reflex is being finalized. As of
604 now, the app comprises of a Main Activity, Instructions
Activ-605 ity and a Camera Activity. It is available in two languages:
606 English and French, both widespread languages in SSA.
607 3.3. Technical validation
608 3.3.1. Video acquisition and image processing
609 During the manual validation (seeFig. 4), the Gamma fitted
610 ratio resulted significantly highly correlated with the manual
611 measurement (Pearson’s r = 0.963, p-value < 0.0001 versus
612 Pearson’s r = 0.982 and p-value < 0.0001 of the raw automated
613 signal (app)), with a RMSE and a MAE of 3.20% and 2.24%,
614 respectively (versus 3.96% and 3.09% of the raw signal).
More-615 over, the error rate for the Gamma fitted ratio resulted to be
616 7.14%.
617 3.3.2. Benchmarking
618 Ten videos acquired by clinical ophthalmologists with the
IR-619 pupillometer on healthy subjects were analyzed with the
IR-620 pupillometer software and with the app algorithms in Matlab.
621 Fig. 5shows the pupil reaction over the frames, captured by
622 the three different algorithms. Resulting measures were
com-623 pared with those calculated by hand after annotating the
624 diameter of the pupil manually for each video-frame. The
625 MAE and RMSE demonstrated a significant improvement in
626 comparison with the software provided with the commercial
627 device, for all the variables, as reported inTable 1. The
agree-628 ment among the measurement methods, namely app
algo-629 rithms/IR method, app Algorithms/Manual method and IR
630 method/Manual method, was estimated with Bland-Altman
631 plots[57,58](plots are not reported for brevity, but are
avail-632 able upon request), following the Shapiro-Wilk test for
nor-633 mality[50].
634
All the differences imputed to the Bland-Altman plot
635
resulted normally distributed with a 0.842 < W < 1 [59]at a
636
95% confidence level.Table 2reports the 95% limits of
agree-637
ment for each variable (the lower the better). The agreement
638
between the app and the manual method outperformed the
639
other methods.
640 3.3.3. Testing the safety of the flash
641
The base illuminance (i.e., the one of the ambient) was
mea-642
sured at 200 lx; since the illuminance in the ‘‘flash on”-state
643
was 680 lx, the illuminance of the flash alone was 480 lx.
644
The comparison of this value to the above-mentioned ISO
645
standards ensured the safety of the procedure. In fact,
646
480 lx convert to 7.0310-5W/cm2under the hypothesis of an
647
average wavelength of 555 nm (the ISO standards set the
648
max allowed value to 0.706 W/cm2).
649
4.
Discussion
650
This paper presented the design and technical validation of
651
an app for the measurement of the pupillary reflex, intended
652
to be used in LRSs. Given the absence of specific regulations or
653
clear guidelines for the design of medical devices for LRSs, we Fig. 4 – A comparison of the signals: the manual
measurements (dashed gray line), the raw automated algorithm (dashed orange line) and the Gamma-fitted ratio (yellow line).
Fig. 5 – The normalized pupil diameter over the frames. Superimposed on the curve, 5 sample snapshots acquired during the app testing. Each snapshot comprises of two halves, highlighted by a yellow semi-circle: each left half represents the initial condition, and each right half represents the evolution of the response.
Table 1 – Values of the mean absolute error and root mean square error for the IR pupillometer and for our solution.
Parameter MAE RMSE
Pupil Minimum (%) IR 1.11 1.55
app 1.00 1.36
Max Constriction Velocity (%/s) IR 6.76 8.07
app 2.56 3.26
Mean Constriction Velocity (%/s) IR 2.85 3.70
app 0.47 0.70
Mean Dilation Velocity (%/s) IR 6.84 7.35
654 adopted the prescriptions of the European regulations on
655 medical devices, the relevant standards for designing
smart-656 phone applications, and the 5A principle of the WHO.
657 The first part of this paper illustrated how the local needs
658 and contextual analyses can be performed enriching
engi-659 neering design with ethnographic methods. The second part
660 presented and discussed the technical validation of the
soft-661 ware, which was performed in two steps: validation of the
662 acquisition and benchmarking of our app versus a
commer-663 cial IR-pupilometer assuming as gold-standard the
frame-664 to-frame manual annotation of pupillary video recordings
665 from 10 subjects. The very low errors and high correlation
666 resulting from the former validation confirmed that a
667 smartphone-based pupillometry acquisition without
acces-668 sories was viable. This concept was corroborated by the low
669 errors and narrow limits of agreement for the variables
result-670 ing from the second validation. The latter proved that the
pro-671 posed solution, despite being based on a simple app and a
672 smartphone in order to be sustainable in resource-scarce
set-673 tings, is able to perform just as well, and often better than the
674 benchmark. These results were possible due to the
interpola-675 tion algorithm and the normalization of the pupil diameter
676 with the iris one, which minimised artefacts due to hand
677 motions and the use of visible light for pupil stimulation via
678 mobile phone flash and video acquisition. In fact, commercial
679 pupilometers use IR for image acquisition, which is not
avail-680 able in the majority of smartphones. Indeed, the app
681 described achieved better results than the commercial IR
682 medical device.
683 Moreover, the comparison with existing literature
sug-684 gested that the proposed solution is the only one designed
685 for LMICs and rigorously validated. In 2013, Tae-hoon Kim
686 et al. [18] proposed a smartphone-based pupilometer that
687 works with an Android app and an add-on device, which
con-688 tains two types of LEDs and an IR filter. Their results showed
689 that their system could have been a good candidate for
pupil-690 lometry, however it had not been validated against a
CE-691 marked or FDA-cleared commercial pupilometer. Moreover,
692 the required accessories would make its use in LMICs
incon-693 venient. In 2017, Mariakakis et al.[19]proposed an
iPhone-694 based pupilometer that works with a box similar to the one
695
used for virtual reality headsets and makes use of
convolu-696
tional neural networks. The box was used to eliminate
ambi-697
ent light and control the distance to the person’s face.
698
Nonetheless, the authors themselves claimed that such a
699
box could be a hindrance in case of measuring the pupil light
700
reaction with an unconscious patient and for tracking the
701
whole reaction to the flash (i.e., the dilation phase cannot
702
be captured because of the lack of lighting). Their design, in
703
fact, only allowed assessing the pupil constriction phase
704
and seems to require a server connection in order to work.
705
In 2018, McAnany et al.[20]performed a study proving that
706
the iPhone camera could be used for this purpose, comparing
707
it with an IR camera, which was not medical rated. In 2019,
708
the start-up Brightlamp introduced an iPhone app for tracking
709
the photopupillary reflex based on trained object detectors
710
and on the use of no accessory. Such app was manually
vali-711
dated similarly to part two of our validation with no
bench-712
marking, resulting in a higher MAE (2.9%) and wider limits
713
of agreement for the pupil constriction (±14%, which
714
improved to ± 9% after bias correction). However, a recent
715
study by McKay et al.[60]benchmarked Brightlamp with a
716
portable IR pupillometer demonstrating that this particular
717
iPhone app has poor repeatability and is not practical tool
718
for supporting clinical decisions. Nonetheless, in general,
719
iPhone-based pupillometry, relying on Hough transform,
720
was proved to be possible and accurate enough by Neice
721
et al.[61].
722
Moreover, the use of iPhones in SSA is quite uncommon
723
due to their cost and because iPhone does not have any
724
rugged model. In 2019, Viga`rio, et al.[21]proposed a system
725
for the continuous monitoring of the pupil using a
smart-726
phone, the Virtoba support for mobile-phones and two LEDs.
727
However, the system does not provide the typical
pupillome-728
try stimulus (i.e., flash in the eye) and its validation was
lim-729
ited to physiological data found in literature (i.e., the reaction
730
of the pupils to a cold stress test). None of the designs above
731
were conceived for LRSs. As it emerged from our contextual
732
analysis, in fact, basing part of the design on extra add-on
733
parts can turn out to be counterproductive either in a possible
734
early health technology assessment phase or when already
735
on the market. Adding extra add-on devices will increase Table 2 – Values of agreement between methods are shown for each of the four variables.
Variable Limits of Agreement
Pupil minimum app-IR ±2.74%
app-Man ±1.99%
IR – Man ±3.03%
Max constriction velocity app-IR ±9.48%/s
app-Man ±6.33%/s
IR – Man ±11.74%/s
Mean constriction velocity app-IR ±7.42%/s
app-Man ±1.23%/s
IR – Man ±7.52%/s
Mean dilation velocity app-IR ±5.61%/s
app-Man ±0.20%/s
736 the need for spare parts that will probably not be available in
737 LRSs. For this reason, the authors of this paper suggest that
738 the ‘‘less is more” philosophy should be adopted when
start-739 ing considering additional parts of a device conceived for
740 these settings. Although the study was focused on
pupillom-741 etry, its findings on the design can be relevant for other
appli-742 cations. For instance, it emerged clearly that affordability is
743 not the only criteria for a device to be suitable for LRSs. Many
744 other issues should be considered during the design,
includ-745 ing affordance, easiness of deployment and use, resilience
746 to underlying infrastructures that could be not stable,
avail-747 ability of spare parts and consumables, and available
underly-748 ing technologies.
749 Another issue that emerged is the tendency to release
750 apps with healthcare ambitions without proper technical
val-751 idations (e.g., manual and/or benchmarking for apps). In the
752 past years, both the FDA and the European Commission
equa-753 ted medical software (including app) to medical devices,
mak-754 ing validation essential to guarantee safety and adequate
755 performance. For this reason, in this paper, we decided to
756 adopt the European perspective for CE marking medical apps,
757 in order to stress the importance of the technical validation
758 phase in the design cycle.
759
5.
Limitations
760 This study presents the preliminary results of the design and
761 technical validation of a smartphone app.
762 The results are valid and limited to one Android
smart-763 phone model; further testing could include more models. In
764 these further tests the different flashes of different
smart-765 phone models should be checked for safety against the
rele-766 vant standards.
767 The current design relies on a server connection, which
768 may be a bottleneck, although currently many remote areas
769 of LRSs (e.g., Africa) are served by good quality mobile phone
770 services. To overcome these limitations, future versions of the
771 app will also include the processing algorithms. To this
772 regard, also artificial intelligence may be explored. While this
773 solution may be difficult to run on very old smartphones, it
774 should run smoothly on the other models.
775 Furthermore, a possible bias in the feasibility study might
776 have been introduced because the opposite eye was not
cov-777 ered and could have potentially been partially stimulated by
778 the changes in the ambient light. However, the ambient light
779 was measured and maintained as constant as possible
780 throughout the experiment. To this regard, healthcare
work-781 ers will need to be instructed and cover the opposite eye in
782 order to avoid bias in the pupillary reactions.
783 Moreover, as of now, the app is not giving any result in
784 terms of millimetres; future versions may include this feature
785 only for the pupil size, as it would be redundant for the pupil/
786 iris ratio.
787 Finally, the performance of the app is currently evaluated
788 on light brown eyes, darker shades should be investigated,
789 as they may be more challenging for pupillometers relying
790 on visible light only. Future experiments could test the
appli-791 cation on subjects with three types of iris colour (i.e., fair,
792
medium, and dark). In this way, the efficiency of our
applica-793
tion on different iris colours could be evaluated. This could
794
also inform future upgrades of the app software to make it
795
more efficient.
796
6.
Conclusions
797
This paper presented the design and technical validation of a
798
mobile app aimed to perform smartphone-based
pupillome-799
try, suitable for use in LMICs. The performance of the app
800
algorithm is promising and, being able to compete with the
801
performance of the algorithm of a commercial IR
pupillome-802
ter medical device, suggests furthering the study with more
803
smartphone models and transitioning towards a dedicated
804
server application and/or a completely standalone app.
805
The performance of the algorithms of the app, as
con-806
firmed by the technical validation, are sound: the proposed
807
solution, by exploiting the pervasive presence of smartphones
808
in LMICs and by not requiring expensive settings or complex
809
procedures, represents a significant improvement towards an
810
extensive screening of eye pathologies and brain trauma
811
worldwide.
812
Declarations
813 Ethics approval and consent to participate
814
The ethnographic analysis was conducted in accordance with
815
the ethical approval REGO-2018-2283, obtained from the
816
Biomedical and Scientific Research Ethics Committee.
817
The videos used for the technical validation were acquired
818
from 11 healthy subjects in accordance with the ethical
819
approval obtained from the Ethical Committee of University
820
of Campania Luigi Vanvitelli (Registration number 500,
821
approval title ‘‘Studio pilota sull’utilizzo della pupillometria
cro-822
matica per la diagnosi e il monitoraggio delle degenerazioni retiniche
823
ereditarie”).
824 Consent for publication
825
Not applicable.
826 Availability of data and materials
827
All the data and materials are available upon reasonable
828
request.
829 Competing interests
830
The authors declare no competing interest.
831 Funding
832
D. Piaggio and L. Pecchia received funding from the University
833
of Warwickwith two Warwick Impact Found grants supported Q3
834
by the EPSRC Impact Accelerator Award (EP/K503848/1 and EP/
835
836
CRediT authorship contribution statement
837 Davide Piaggio: Methodology, Formal analysis, Investigation,
838 Visualization. Georgy Namm: Formal analysis, Investigation,
839 Data curation, Visualization. Paolo Melillo: Methodology,
840 Investigation, Resources. Francesca Simonelli: Methodology,
841 Resources. Ernesto Iadanza: Conceptualization, Methodology,
842 Resources, Supervision. Leandro Pecchia: Conceptualization,
843 Methodology, Investigation, Resources, Visualization,
Super-844 vision, Project administration, Funding acquisition.
845
Acknowledgement
846 None.
8 4 7
R E F E R E N C E S
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