Dataset Information

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

ABSTRACT:

Purpose

To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.

Design

DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset.

Participants

A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants).

Methods

Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students.

Main outcome measures

Mean squared error (MSE).

Results

On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC.

Conclusions

DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

SUBMITTER: Keenan TDL

PROVIDER: S-EPMC9038670 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

Keenan Tiarnan D L TDL Chen Qingyu Q Agrón Elvira E Tham Yih-Chung YC Goh Jocelyn Hui Lin JHL Lei Xiaofeng X Ng Yi Pin YP Liu Yong Y Xu Xinxing X Cheng Ching-Yu CY Bikbov Mukharram M MM Jonas Jost B JB Bhandari Sanjeeb S Broadhead Geoffrey K GK Colyer Marcus H MH Corsini Jonathan J Cousineau-Krieger Chantal C Gensheimer William W Grasic David D Lamba Tania T Magone M Teresa MT Maiberger Michele M Oshinsky Arnold A Purt Boonkit B Shin Soo Y SY Thavikulwat Alisa T AT Lu Zhiyong Z Chew Emily Y EY

Ophthalmology 20220103 5

<h4>Purpose</h4>To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.<h4>Design</h4>DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset.<h4>Participants</h4>A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants).<h4>Methods</h4>Deep learning models were trained to detect and quantify ...[more]

PMID: 34990643

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Dataset Information

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

Purpose

Design

Participants

Methods

Main outcome measures

Results

Conclusions

Publications

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

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