Computational methodology for automatic detection of strabismus in digital images through Hirschberg test

Usefulness Assessment of Automated Strabismus Angle Measurements Using Innovative Strabiscan Device

BACKGROUND

The variability of the obtained results of manual tests assessing the angle of strabismus depends on the experience, skills, and training of the examiner. The authors hope that this new measuring device will provide a more sensitive and repeatable method for detecting small strabismus angles compared to the gold standard-PCT. The purpose of this article is to present an innovative strabismus angle demonstration device, called Strabiscan, to provide automated measurements of eye deviation and to compare the obtained results of these measurements to the traditional manual method.

METHODS

For patients with manifest strabismic disease (n = 30) and a group of healthy subjects (n = 30), a detailed history was taken and routine ophthalmologic examinations were performed, including best-corrected distance visual acuity, assessment of refractive error using an autorefractometer after cycloplegia, biomicroscopic evaluation of the anterior segment of the eye and evaluation of the eye fundus by indirect ophthalmoscopy. Subsequently, each patient and healthy subject was subjected to a prismatic cover-uncover test using a manual method, after which the presence of strabismus was detected and its angle assessed using a Strabiscan demonstration device.

RESULTS

In the control group using the Strabiscan demonstration device, small-angle latent strabismus ≤ 3DP was diagnosed in 83% of patients, while >3DP was found in 13%. In contrast, using the prismatic cover-uncover test, latent strabismus ≤ 3DP was diagnosed in only 13% of patients, and latent strabismus with an angle > 3DP was found in 13% of patients. No statistically significant differences were noted in the measurements of strabismus angles made by the different methods.

CONCLUSIONS

The Strabiscan demonstration device allows quick and accurate assessment of the strabismus angle. Compared to the prismatic cover-uncover test, it has a higher sensitivity for detecting low-angle latent strabismus. Measurements with the Strabiscan do not require the presence of additional assistants for the test.

A new method based on deep learning and image processing for detection of strabismus with the Hirschberg test

Strabismus is a condition in which one or both eyes do not work in parallel or in harmony. People with strabismus have one eye looking straight ahead while the other eye looks inwards, outwards, upwards or downwards. This condition can affect both eyes. Strabismus is a common eye condition that affects about 4 % of the world's population. Tests such as Hirschberg, Cover and Krimsky are used to detect strabismus. In the Hirschberg test, a light source is held at a distance of 50 cm so that it falls on the centre of each eye. The horizontal and vertical distance between the centre of gravity of the light reflected from the cornea and the centre of the pupil indicates the degree of strabismus. In this study, deep learning and image processing algorithms are used to detect the eye, corneal reflection, iris and pupil on a patient's facial image. Based on the Hirschberg test, the horizontal and vertical shifts for both eyes were measured to determine the patient's degree of strabismus. In this way, the Hirschberg test used in strabismus screening was performed automatically by software. The correct detection of the pupil and the light reflected from the cornea by the algorithm means that the eye has been measured correctly. The software was tested on the facial images of 88 strabismic patients of different sexes and ages. 91 % of the 88 patients, or 80 patients, had their left eye measured correctly. 90 % of the 88 patients, or 79 patients, had their right eye measured correctly. The results for each eye obtained from the correct measurements were found to have an error of maximum ± 2°. This error is due to the fact that a real eye is in three-dimensional space, while the digital eye image is in two-dimensional space, and was only observed in the test results of some patients. This algorithm can be tested on patients of all ages and is not affected by morphological differences in the patients' faces. Successful results have been observed experimentally that this newly proposed method can be used in strabismus screening.

An improved strabismus screening method with combination of meta-learning and image processing under data scarcity

Purpose

Considering the scarcity of normal and strabismic images, this study proposed a method that combines a meta-learning approach with image processing methods to improve the classification accuracy when meta-learning alone is used for screening strabismus.

Methods

The meta-learning approach was first pre-trained on a public dataset to obtain a well-generalized embedding network to extract distinctive features of images. On the other hand, the image processing methods were used to extract the position features of eye regions (e.g., iris position, corneal light reflex) as supplementary features to the distinctive features. Afterward, principal component analysis was applied to reduce the dimensionality of distinctive features for integration with low-dimensional supplementary features. The integrated features were then used to train a support vector machine classifier for performing strabismus screening. Sixty images (30 normal and 30 strabismus) were used to verify the effectiveness of the proposed method, and its classification performance was assessed by computing the accuracy, specificity, and sensitivity through 5,000 experiments.

Results

The proposed method achieved a classification accuracy of 0.805 with a sensitivity (correct classification of strabismus) of 0.768 and a specificity (correct classification of normal) of 0.842, whereas the classification accuracy of using meta-learning alone was 0.709 with a sensitivity of 0.740 and a specificity of 0.678.

Conclusion

The proposed strabismus screening method achieved promising classification accuracy and gained significant accuracy improvement over using meta-learning alone under data scarcity.

Determining vertical fusion values from digital photographs of healthy eyes

PURPOSE

To determine the mean difference of vertical corneal light reflex (VCLR) among healthy eyes via digital photography.

STUDY DESIGN

Retrospective study.

METHODS

The study enrolled 155, healthy eyes participants, 71 males and 84 females with a mean age of 14.7 years (range 12-19 years). The participants received complete eye examinations and 2 digital photographs were taken, with the flash on, while participants fixated their eyes on a near and a distant target. Two hundred and eighty qualified photographs were analyzed by Photo-Hirschberg testing using computer software. The vertical corneal light reflex ratio (VCLRR) was calculated as the distance of the corneal light reflex (CLR) to the inferior limbus or to the pupillary border divided by the horizontal corneal diameter, defined as VCLRR1 or VCLRR2. VCLRR was analyzed using Spearman's correlation.

RESULTS

The mean ± SD of horizontal corneal diameter in near and distance photographs was 11.47 ± 0.62 and 11.37 ± 0.58 mm, respectively. For correlation analysis, at 1 m fixation and 6 m fixation, the number of participants within an acceptable range of vertical fusion were 94.6% and 100% of participants. The 95th percentiles (estimated as the mean ± 1.64SD) in VCLRR1 between the two eyes at near and at distance fixation were 0.0316 and 0.0272, respectively; whereas the corresponding values for VCLRR2 were 0.0309 and 0.0240, respectively.

CONCLUSIONS

The normal range of the vertical corneal light reflex ratio suggests that the Photo-Hirschberg test could be used for screening vertical strabismus cases depending on iris pigment.

A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work

BACKGROUND

With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required.

OBJECTIVES

This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications.

METHODS

An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigate the databases of Science Direct, IEEE Xplore, Web of Science, PubMed, and Medline for the articles, which have been published from January 2012 up to date.

RESULTS

A new crossover matching between machine learning methods and telemedicine taxonomy is proposed. The crossover-taxonomy is developed in this study to identify the relationship between machine learning algorithm and the equivalent telemedicine categories whereas the machine learning algorithm has been utilized. The impact of utilizing machine learning is composed in proposing the telemedicine architecture based on synchronous (real-time/ online) and asynchronous (store-and-forward / offline) structure. In addition to that, list of machine learning algorithms, list of the performance metrics, list of inputs data and outputs results are presented. Moreover, open research challenges, the benefits of utilizing machine learning and the recommendations for new research opportunities that need to be addressed for the synergistic integration of multidisciplinary works are organized and presented accordingly.

DISCUSSION

The state-of-the-art studies on the E-triage and priority systems that utilise machine learning algorithms in telemedicine architecture are discussed. This approach allows the researchers to understand the modernisation of healthcare systems and the efficient use of artificial intelligence and machine learning. In particular, the growing worldwide population and various chronic diseases such as heart chronic diseases, blood pressure and diabetes, require smart health monitoring systems in E-triage and priority systems, in which machine learning algorithms could be greatly beneficial.

CONCLUSIONS

Although research directions on E-triage and priority systems that use machine learning algorithms in telemedicine vary, they are equally essential and should be considered. Hence, we provide a comprehensive review to emphasise the advantages of the existing research in multidisciplinary works of artificial intelligence, machine learning and healthcare services.

An automatic screening method for strabismus detection based on image processing

Purpose

This study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility.

Materials and methods

The proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu’s binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening.

Result

We used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively.

Conclusion

The experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility.

A mhealth application for automated detection and diagnosis of strabismus

BACKGROUND AND OBJECTIVE

Amblyopia is a public health problem, and strabismus is its primary cause. Our objective is to evaluate the concordance of the diagnosis of strabismus between strabismus expert ophthalmologist and the mhealth application developed for this purpose.

METHODS

We evaluated the concordance of the diagnosis of strabismus between the expert ophthalmologist and the mhealth application by screening 224 children and adolescents in the 5-15 years age group, with snapshots of patients' eyes and their analysis thereof. We were using a multifunctional cell phone with Android and the ophthalmologist's clinical evaluation by analyzing the ocular deviations using simple cover and prism and alternate cover.

RESULTS

Fraction measurements were used with two cutoff points of 6 and 11 prismatic diopters (PD). Results were compared according to their concordances, with a fair Kappa equal to 0.43 (95%CI = [0.38; 0.48]), which was statistically significant (p < 0.0001) at the cutoff point of 6 PD and Kappa equal to 0.49 (95% CI = [0.35; 0.61]), which was statistically significant (p < 0.042) in the cutoff point of 11 PD.

CONCLUSIONS

The cutoff point of 6 PD was chosen for screening by this mhealth application since it caused the loss of only two patients with strabismus, whereas, in the case of 11 PD, the loss was five patients in the universe of 224. These results are promising for the use of this software as a screening method for patients with strabismus.

Surgical planning of horizontal strabismus using multiple output regression tree

Strabismus is an eye disease that affects about 0.12%-9.86% of the population, which can cause irreversible sensory damage to vision and psychological problems. The most severe cases require surgical intervention, despite other less invasive techniques being available for a more conservative approach. As for surgeries, the treatment goal is to align the eyes to recover binocular vision, which demands knowledge, training, and experience. One of the leading causes of failure is human error during the measurement of deviation. Thus, this work proposes a new method based on the Decision Tree Regressor algorithms to assist in the surgical planning for horizontal strabismus to predict recoil and resection measures in the lateral and medial rectus muscles. In the presented method, two application approaches were taken, being in the form of multiple single target models, one procedure at a time, and the form of one multiple target model or all surgical procedures together. The method's efficiency is indicated by the average difference between the value indicated by the method and the physician's value. In our most accurate model, an average error of 0.66 mm was obtained for all surgical procedures, both for resection and recoil in the indication of the horizontal strabismus surgical planning. The results present the feasibility of using Decision Tree Regressor algorithms to perform the planning of strabismus surgeries, making it possible to predict correction values for surgical procedures based on medical data analysis and exceeding state-of-art.

Evaluation of a Hirschberg Test-Based Application for Measuring Ocular Alignment and Detecting Strabismus

Purpose: Photographic Hirschberg test applications are practical options for screening in areas where a specialist is not available. A semi-automated Hirschberg test-based application was developed and evaluated on its ability to detect and measure strabismus at distance and near fixation.Methods: This is a prospective cross-sectional inter-rater agreement study conducted at a tertiary hospital. Study A evaluated the ability of the application to determine the presence or absence of strabismus in subjects of unknown strabismus status (n = 28). Study B evaluated the ability of the application to measure the deviation of strabismic subjects (n = 8). All subjects underwent alternate prism cover test (APCT) at distance and near fixation. Facial photographs at distance and near fixation were taken. Each photograph underwent automated face and eye detection, manual limbus and corneal reflex identification, and strabismus detection and measurement.Results: The application obtained a matching rate of 95.14% for the face and eyes. The application yielded a sensitivity of 92.86% for horizontal strabismus at distance and near fixation, however, with low specificity values (7.692%, 14.81%, and 8%). The Bland-Altman plots derived from Study B showed bias values of application measurements between 3.625Δ and 6.125Δ with wide intervals of the limits of agreement. Repeatability of the measurements yielded bias values of -0.625Δ and 2.5Δ for horizontal and vertical strabismus at distance and 4.375Δ and 1.25Δ at near fixation, respectively.Conclusion: This semi-automated Hirschberg test-based application can effectively determine the face and eye location and shows potential as a screening tool for horizontal strabismus.

A smartphone ocular alignment measurement app in school screening for strabismus

Background

Strabismus is the leading risk factor for amblyopia, which should be early detected for minimized visual impairment. However, traditional school screening for strabismus can be challenged due to several factors, most notably training, mobility and cost. The purpose of our study is to evaluate the feasibility of using a smartphone application in school vision screening for detection of strabismus.

Methods

The beta smartphone application, EyeTurn, can measure ocular misalignment by computerized Hirschberg test. The application was used by a school nurse in a routine vision screening for 133 elementary school children. All app measurements were reviewed by an ophthalmologist to assess the rate of successful measurement and were flagged for in-person verification with prism alternating cover test (PACT) using a 2.4Δ threshold (root mean squared error of the app). A receiver operating characteristic (ROC) curve was used to determine the best sensitivity and specificity for an 8Δ threshold (recommended by AAPOS) with the PACT measurement as ground truth.

Results

The nurse obtained at least one successful app measurement for 93% of children (125/133). 40 were flagged for PACT, of which 6 were confirmed to have strabismus, including 4 exotropia (10△, 10△, 14△ and 18△), 1 constant esotropia (25△) and 1 accommodative esotropia (14△). Based on the ROC curve, the optimum threshold for the app to detect strabismus was determined to be 3.0△, with the best sensitivity (83.0%), specificity (76.5%). With this threshold the app would have missed one child with accommodative esotriopia, whereas conventional screening missed 3 cases of intermittent extropia.

Conclusions

Results support feasibility of use of the app by personnel without professional training in routine school screenings to improve detection of strabismus.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-021-01902-w.

An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos

Background

Strabismus affects approximately 0.8–6.8% of the world’s population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice.

Methods

An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020.

Results

In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1–99.7%], a specificity of 98.3% (95% CI, 94.6–99.5%), and an AUC of 0.998 (95% CI, 0.993–1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion.

Conclusions

The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.

A One-Step, Streamlined Children's Vision Screening Solution Based on Smartphone Imaging for Resource-Limited Areas: Design and Preliminary Field Evaluation

BACKGROUND

Young children's vision screening, as part of a preventative health care service, produces great value for developing regions. Besides yielding a high return on investment from forestalling surgeries using a low-cost intervention at a young age, it improves school performance and thus boosts future labor force quality. Leveraging low-skilled health care workers with smartphones and automated diagnosis to offer such programs can be a scalable model in resource-limited areas.

OBJECTIVE

This study aimed to develop and evaluate an effective, efficient, and comprehensive vision screening solution for school children in resource-limited areas. First, such an exam would need to cover the major risk factors of amblyopia and myopia, 2 major sources of vision impairment effectively preventable at a young age. Second, the solution must be integrated with digital patient record-keeping for long-term monitoring and popular statistical analysis. Last, it should utilize low-skilled technicians and only low-cost tools that are available in a typical school in developing regions, without compromising quality or efficiency.

METHODS

A workflow for the screening program was designed and a smartphone app was developed to implement it. In the standardized screening procedure, a young child went through the smartphone-based photoscreening in a dark room. The child held a smartphone in front of their forehead, displaying pre-entered personal information as a quick response code that duplexed as a reference of scale. In one 10-second procedure, the child's personal information and interpupillary distance, relative visual axis alignment, and refractive error ranges were measured and analyzed automatically using image processing and artificial intelligence algorithms. The child's risk for strabismus, myopia, and anisometropia was then derived and consultation given.

RESULTS

A preliminary evaluation of the solution was conducted alongside yearly physical exams in Luoyang, Henan, People's Republic of China. It covered 20 students with suspected strabismus and 80 randomly selected students, aged evenly between 8 and 10. Each examinee took about 1 minute, and a streamlined workflow allowed 3 exams to run in parallel. The 1-shot and 2-shot measurement success rates were 87% and 100%, respectively. The sensitivity and specificity of strabismus detection were 0.80 and 0.98, respectively. The sensitivity and specificity of myopia detection were 0.83 and 1.00, respectively. The sensitivity and specificity of anisometropia detection were 0.80 and 1.00, respectively.

CONCLUSIONS

The proposed vision screening program is effective, efficient, and scalable. Compared with previously published studies on utilizing a smartphone for an automated Hirschberg test and photorefraction screening, this comprehensive solution is optimized for practicality and robustness, and is thus better ready-to-deploy. Our evaluation validated the achievement of the program's design specifications.

Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test

Strabismus is a common vision disease that brings about unpleasant influence on vision, as well as life quality. A timely diagnosis is crucial for the proper treatment of strabismus. In contrast to manual evaluation, well-designed automatic evaluation can significantly improve the objectivity, reliability, and efficiency of strabismus diagnosis. In this study, we have proposed an innovative intelligent evaluation system of strabismus in digital videos, based on the cover test. In particular, the video is recorded using an infrared camera, while the subject performs automated cover tests. The video is then fed into the proposed algorithm that consists of six stages: (1) eye region extraction, (2) iris boundary detection, (3) key frame detection, (4) pupil localization, (5) deviation calculation, and (6) evaluation of strabismus. A database containing cover test data of both strabismic subjects and normal subjects was established for experiments. Experimental results demonstrate that the deviation of strabismus can be well-evaluated by our proposed method. The accuracy was over 91%, in the horizontal direction, with an error of 8 diopters; and it was over 86% in the vertical direction, with an error of 4 diopters.

Development and Preliminary Evaluation of a Smartphone App for Measuring Eye Alignment

Purpose

We evaluate a smartphone application (app) performing an automated photographic Hirschberg test for measurement of eye deviations.

Methods

Three evaluation studies were conducted to measure eye deviations in the horizontal direction. First, gaze angles were measured with respect to the ground truth in nonstrabismic subjects (n = 25) as they fixated monocularly on targets of known eccentricity covering an angular range of approximately ±13°. Second, phoria measurements with the app at near fixation (distance = 40 cm) were compared with the modified Thorington (MT) test in normally-sighted subjects (n = 14). Third, eye deviations using the app were compared to a cover test with prism neutralization (CTPN; n = 66) and Synoptophore (n = 34) in strabismic subjects. Regression analyses were used to compare the app and clinical measurements of the magnitude and direction of eye deviations (prism diopters, Δ).

Results

The gaze angles measured by the app closely followed the ground truth (slope = 1.007, R² = 0.97, P < 0.001), with a root mean squared error (RMSE) of 2.4Δ. Phoria measurements with the app were consistent with MT (slope = 0.94, R² = 0.97, P < 0.001, RMSE = 1.7Δ). Overall, the strabismus measurements with the app were higher than with Synoptophore (slope = 1.15, R² = 0.91, P < 0.001), but consistent with CTPN (slope = 0.95, R² = 0.95, P < 0.001). After correction of CTPN values for near fixation, the consistency of the app measurements with CTPN was improved further (slope = 1.01).

Conclusions

The app measurements of manifest and latent eye deviations were consistent with the comparator clinical methods.

Translational Relevance

A smartphone app for measurement of eye alignment can be a convenient clinical tool and has potential to be beneficial in telemedicine.

Tests for detecting strabismus in children aged 1 to 6 years in the community

BACKGROUND

Strabismus (misalignment of the eyes) is a risk factor for impaired visual development both of visual acuity and of stereopsis. Detection of strabismus in the community by non-expert examiners may be performed using a number of different index tests that include direct measures of misalignment (corneal or fundus reflex tests), or indirect measures such as stereopsis and visual acuity. The reference test to detect strabismus by trained professionals is the cover‒uncover test.

OBJECTIVES

To assess and compare the accuracy of tests, alone or in combination, for detection of strabismus in children aged 1 to 6 years, in a community setting by non-expert screeners or primary care professionals to inform healthcare commissioners setting up childhood screening programmes.Secondary objectives were to investigate sources of heterogeneity of diagnostic accuracy.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2016, Issue 12) (which contains the Cochrane Eyes and Vision Trials Register) in the Cochrane Library, the Health Technology Assessment Database (HTAD) in the Cochrane Library (2016, Issue 4), MEDLINE Ovid (1946 to 5 January 2017), Embase Ovid (1947 to 5 January 2017), CINAHL (January 1937 to 5 January 2017), Web of Science Conference Proceedings Citation Index-Science (CPCI-S) (January 1990 to 5 January 2017), BIOSIS Previews (January 1969 to 5 January 2017), MEDION (to 18 August 2014), the Aggressive Research Intelligence Facility database (ARIF) (to 5 January 2017), the ISRCTN registry (www.isrctn.com/editAdvancedSearch); searched 5 January 2017, ClinicalTrials.gov (www.clinicaltrials.gov); searched 5 January 2017 and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en); searched 5 January 2017. We did not use any date or language restrictions in the electronic searches for trials. In addition, orthoptic journals and conference proceedings without electronic listings were searched.

SELECTION CRITERIA

All prospective or retrospective population-based test accuracy studies of consecutive participants were included. Studies compared a single or combination of index tests with the reference test. Only those studies with sufficient data for analysis were included specifically to calculate sensitivity and specificity and determine diagnostic accuracy.Participants were aged 1 to 6 years. Studies reporting participants outside this range were included if subgroup data were available.Permitted settings included population-based vision screening programmes or opportunistic screening programmes, such as those performed in schools.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. In brief, two review authors independently assessed titles and abstracts for eligibility and extracted the data, with a third senior author resolving any disagreement. We analysed data primarily for specificity and sensitivity.

MAIN RESULTS

One study from a total of 1236 papers, abstracts and trials was eligible for inclusion with a total number of participants of 335 of which 271 completed both the screening test and the gold standard test. The screening test using an automated photoscreener had a sensitivity of 0.46 (95% confidence interval (CI) 0.19 to 0.75) and specificity of 0.97 (CI 0.94 to 0.99). The overall number affected by strabismus was low at 13 (4.8%).

AUTHORS' CONCLUSIONS

There is very limited data in the literature to ascertain the accuracy of tests for detecting strabismus in the community as performed by non-expert screeners. A large prospective study to compare methods would be required to determine which tests have the greatest accuracy.

Rate of Strabismus Detection on Digital Photographs Increases by Using Off-center Near Target

PURPOSE

To increase the detection rate of strabismus on digital photographs, with the ultimate aim of developing a new automated strabismus detection algorithm.

METHODS

In this prospective case series, the authors acquired digital face photographs of 409 children with manifest or latent strabismus, using a 14-million-pixel camera with CCD image sensor. Of the last 52 enrolled, 34 image sets were selected for this study: 29 with manifest and 5 with latent strabismus. Images were taken at a distance of 40 to 70 cm in primary position, with the camera lens as the fixation target and in slight off-center fixation, and using a novel target of small light-emitting diodes mounted onto the camera case. The location of the corneal light reflection was manually calculated in relation to the center of the pupil in both eyes and ocular deviation as the difference in corneal light reflection location between the two eyes. In orthotropia, the expected deviation is zero.

RESULTS

In children with phorias, the mean corneal light reflection location difference between the eyes was -0.10 ± 0.14 mm in primary position and -2.02 ± 0.39 mm in off-center fixation. Using a threshold of ±0.5 mm on either side of zero for central and of 2 mm for off-center fixation, sensitivity to detect strabismus increased from 65.6% in central to 79.3% in off-center fixation, respectively. The calculation of specificity will require inclusion of a population of individuals without strabismus.

CONCLUSIONS

Off-center fixation onto a near target ensures that participants are actively looking at the target and may increase accommodative effort, thereby increasing the detection rate of strabismus. [J Pediatr Ophthalmol Strabismus. 2017;54(2):90-96.].

Computer-Aided Methodology for Syndromic Strabismus Diagnosis

Strabismus is a pathology that affects approximately 4 % of the population, causing aesthetic problems reversible at any age and irreversible sensory alterations that modify the vision mechanism. The Hirschberg test is one type of examination for detecting this pathology. Computer-aided detection/diagnosis is being used with relative success to aid health professionals. Nevertheless, the routine use of high-tech devices for aiding ophthalmological diagnosis and therapy is not a reality within the subspecialty of strabismus. Thus, this work presents a methodology to aid in diagnosis of syndromic strabismus through digital imaging. Two hundred images belonging to 40 patients previously diagnosed by an specialist were tested. The method was demonstrated to be 88 % accurate in esotropias identification (ET), 100 % for exotropias (XT), 80.33 % for hypertropias (HT), and 83.33 % for hypotropias (HoT). The overall average error was 5.6Δ and 3.83Δ for horizontal and vertical deviations, respectively, against the measures presented by the specialist.