Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study

Wagner, Siegfried; Liefers, Bart; Radia, Meera; Zhang, Gongyu; Struyven, Robbert; Faes, Livia; Than, Jonathan; Balal, Shafi; Hennings, Charlie; Kilduff, Caroline L S; Pooprasert, Pakinee; Glinton, Sophie; Arunakirinathan, Meena; Giannakis, Periklis; Braimah, Imoro Zeba; Ahmed, Islam S H; Al-Feky, Mariam; Khalid, Hagar; Ferraz, Daniel; Vieira, Juliana Guerra; Jorge, Rodrigo; Husain, Shahid; Ravelo, Janette; Hinds, Anne-Marie; Henderson, Robert J.; Patel, Himanshu; Ostmo, Susan; Campbell, J. Peter; Pontikos, Nikolas; Patel, Praveen J; Keane, Pearse Andrew; Adams, Gill; Balaskas, Konstantinos

doi:10.1016/s2589-7500(23)00050-x

Cited by 7 publications

(1 citation statement)

References 47 publications

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“…It has been successfully used to diagnose a variety of ocular conditions, most notably diabetic retinopathy (21). Recently, quite a few studies have been published on the use of DL to screen for plus disease in ROP (4,(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). However, as a first step, there is a need for an automated tool that can screen the huge at-risk ROP population in India.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Rao,

Savoy,

Tan

et al. 2023

Front. Pediatr.

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PurposeThe primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP).ParticipantsImages were collected from infants enrolled in the KIDROP tele-ROP screening program.MethodsWe developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (n = 25,982, 69.33%), validation (n = 4,006, 10.69%), and an independent test set (n = 7,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1–3) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist.ResultsOf the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%–92.59%) and 91.22% (95% CI: 90.42%–91.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%–83.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%–96.61%) and the AUROC was 0.970.ConclusionThe novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.

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Section: Introductionmentioning

confidence: 99%

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Rao,

Savoy,

Tan

et al. 2023

Front. Pediatr.

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Comparing code-free and bespoke deep learning approaches in ophthalmology

Wong,

O’Byrne,

Taribagil

et al. 2024

Graefes Arch Clin Exp Ophthalmol

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Aim Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writing code. In this review, we comprehensively review the advantages that CFDL offers over bespoke expert-designed deep learning (DL). As exemplars, we use the following tasks: (1) diabetic retinopathy screening, (2) retinal multi-disease classification, (3) surgical video classification, (4) oculomics and (5) resource management. Methods We performed a search for studies reporting CFDL applications in ophthalmology in MEDLINE (through PubMed) from inception to June 25, 2023, using the keywords ‘autoML’ AND ‘ophthalmology’. After identifying 5 CFDL studies looking at our target tasks, we performed a subsequent search to find corresponding bespoke DL studies focused on the same tasks. Only English-written articles with full text available were included. Reviews, editorials, protocols and case reports or case series were excluded. We identified ten relevant studies for this review. Results Overall, studies were optimistic towards CFDL’s advantages over bespoke DL in the five ophthalmological tasks. However, much of such discussions were identified to be mono-dimensional and had wide applicability gaps. High-quality assessment of better CFDL applicability over bespoke DL warrants a context-specific, weighted assessment of clinician intent, patient acceptance and cost-effectiveness. We conclude that CFDL and bespoke DL are unique in their own assets and are irreplaceable with each other. Their benefits are differentially valued on a case-to-case basis. Future studies are warranted to perform a multidimensional analysis of both techniques and to improve limitations of suboptimal dataset quality, poor applicability implications and non-regulated study designs. Conclusion For clinicians without DL expertise and easy access to AI experts, CFDL allows the prototyping of novel clinical AI systems. CFDL models concert with bespoke models, depending on the task at hand. A multidimensional, weighted evaluation of the factors involved in the implementation of those models for a designated task is warranted.

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Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels

Kirik,

Iskandarov,

Erturk

et al. 2024

Photodiagnosis and Photodynamic Therapy

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Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study

Cited by 7 publications

References 47 publications

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Comparing code-free and bespoke deep learning approaches in ophthalmology

Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels

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