EyeScreen

Bernard, Alec; Xia, Shang Zhou; Saleh, Sahal; Ndukwe, Tochukwu; Meyer, Joshua; Soloway, Elliot; Sintayehu, Mandefro; Teshome, Blen; Tadegegne, Bezawit; Nelson, Christine C.; Demirci, Hakan

doi:10.1016/j.xops.2022.100158

Cited by 10 publications

(10 citation statements)

References 18 publications

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“…New software technologies like CRADLE and MDEyeCare have been developed to detect leukocoria, the main and most early signal of RB 21 . Using this principle, Bernard et al 19 . with their EyeScreen mobile application, Vargas‐Cuentas 22 with RetinoApp, and Khedekar et al 21 .…”

Section: Resultsmentioning

confidence: 99%

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Strategies to improve diagnosis and access to treatment of retinoblastoma in low‐ and middle‐income countries: A systematic review

Rabelo,

de Alvarenga,

Fernando Lopes

et al. 2024

Pediatric Blood & Cancer

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Retinoblastoma, the most common intraocular tumor in childhood, still faces challenges in diagnosis and treatment, particularly in low‐ and middle‐income countries. Identifying strategies to improve the time to diagnosis and access to treatment is crucial to enhance survival rates and preserve ocular health. We conducted a systematic review to identify interventions that have demonstrated potential in addressing these challenges. We performed a comprehensive search across databases until March 2023. Out of the studies reviewed, 21 met the inclusion criteria and were categorized into five main areas: surveillance strategies, genetic counseling, education, public assistance, and international partnership. Despite the obstacles faced, the initiatives identified in this review present acts toward improving the time to diagnosis and access to treatment for retinoblastoma. Based on the extracted data, we propose a comprehensive chain of initiatives. We firmly believe that implementing this chain of initiatives can lead to improved clinical outcomes for retinoblastoma patients.

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Section: Resultsmentioning

confidence: 99%

“…Among the 12 cross-sectional studies evaluated, most demonstrated moderate to high methodological quality. 9,17,19,21,22,24,26,28,29,31 The primary issues identified pertained to insufficient details in the study sample and a failure to identify strategies for managing confounding factors.…”

Section: Assessment Of the Risk Of Bias Of The Included Studiesmentioning

confidence: 99%

Strategies to improve diagnosis and access to treatment of retinoblastoma in low‐ and middle‐income countries: A systematic review

Rabelo,

de Alvarenga,

Fernando Lopes

et al. 2024

Pediatric Blood & Cancer

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“…In the last decade, advances have been made in the use of deep-learning to detect pediatric ophthalmic diseases. Deep-learning methods, such as deep convolutional neural networks (DCNNs), have achieved high accuracy in the detection of strabismus, [11] leukocoria, [12] and retinopathy of prematurity, comparable to that of specialists. [13] However, systems speci cally designed to screen for childhood glaucoma have not been reported.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Approach to Detect Childhood glaucoma Based on Periocular Photograph

Kitaguchi

Hayakawa

Kawashima

et al. 2022

Preprint

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Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate and evaluate the performance of a deep-learning (DL) model for detecting childhood glaucoma based on periocular photographs. Primary gaze photographs of children diagnosed with glaucoma with appearance features (corneal opacity, corneal enlargement, and/or globe enlargement) were retrospectively collected from the database of a single referral center. DL framework with the RepVGG architecture was used to automatically recognize childhood glaucoma from photographs. The average receiver operating characteristic curve (AUC) of 5-fold cross-validation was 0.91. When the 5-fold result was assembled, the DL model achieved an AUC of 0.95 with a sensitivity of 0.85 and specificity of 0.94. The DL model showed comparable accuracy to the pediatric ophthalmologists and glaucoma specialists in diagnosing childhood glaucoma (0.90 vs 0.81, p = 0.22, chi-square test), outperforming the average of human examiners in the detection rate of childhood glaucoma in cases without corneal opacity (72% vs. 34%, p = 0.038, chi-square test), with a bilateral corneal enlargement (100% vs. 67%, p = 0.03), and without skin lesions (87% vs. 64%, p = 0.02). Hence, this DL model is a promising tool for diagnosing missed childhood glaucoma cases.

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“…We are very pleased to see that ML is gradually being used in clinical medicine, and that the combination of medicine and engineering is opening up more possibilities for clinical practice. Brain age, ophthalmology and dental age have all been studied with ML ( 12 , 13 , 15 ). For adolescent children, the mean absolute error (MAE) for age estimation using ML was < 1 year in all cases ( 11 , 13 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods

Shen

Yuan

Wang

et al. 2022

Front. Public Health

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BackgroundMachine learning (ML) algorithms play a key role in estimating dental age. In this study, three ML models were used for dental age estimation, based on different preprocessing methods.AimThe seven mandibular teeth on the digital panorama were measured and evaluated according to the Cameriere and the Demirjian method, respectively. Correlation data were used for decision tree (DT), Bayesian ridge regression (BRR), k-nearest neighbors (KNN) models for dental age estimation. An accuracy comparison was made among different methods.Subjects and methodsWe analyzed 748 orthopantomographs (392 males and 356 females) from eastern China between the age of 5 and 13 years in this retrospective study. Three models, DT, BRR, and KNN, were used to estimate the dental age. The data in ML is obtained according to the Cameriere method and the Demirjian method. Coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE), the above five metrics were used to evaluate the accuracy of age estimation.ResultsOur experimental results showed that the prediction accuracy of dental age was affected by ML algorithms. MD, MAD, MSE, RMSE of the dental age predicted by ML were significantly decreased. Among all the methods, the KNN model based on the Cameriere method had the highest accuracy (ME = 0.015, MAE = 0.473, MSE = 0.340, RMSE = 0.583, R2 = 0.94).ConclusionThe results show that the prediction accuracy of dental age is influenced by ML algorithms and preprocessing method. The KNN model based on the Cameriere method was able to infer dental age more accurately in a clinical setting.

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EyeScreen

Cited by 10 publications

References 18 publications

Strategies to improve diagnosis and access to treatment of retinoblastoma in low‐ and middle‐income countries: A systematic review

Strategies to improve diagnosis and access to treatment of retinoblastoma in low‐ and middle‐income countries: A systematic review

Deep-Learning Approach to Detect Childhood glaucoma Based on Periocular Photograph

Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods

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