Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study,British Journal of Ophthalmology

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Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study
British Journal of Ophthalmology ( IF 3.7 ) Pub Date : 2024-11-01 , DOI: 10.1136/bjo-2023-323871
Ziqi Tang ₁ , Xi Wang _{2,

3} , An Ran Ran ₁ , Dawei Yang ₁ , Anni Ling ₁ , Jason C Yam _{1,

4} , Xiujuan Zhang ₁ , Simon K H Szeto _{1,

4} , Jason Chan _{1,

4} , Cherie Y K Wong _{1,

4} , Vivian W K Hui _{1,

4} , Carmen K M Chan _{1,

4} , Tien Yin Wong _{5,

6} , Ching-Yu Cheng _{7,

8} , Charumathi Sabanayagam _{7,

9} , Yih Chung Tham _{7,

8} , Gerald Liew ₁₀ , Giridhar Anantharaman ₁₁ , Rajiv Raman ₁₂ , Yu Cai ₁₃ , Haoxuan Che ₁₄ , Luyang Luo ₃ , Quande Liu ₃ , Yiu Lun Wong ₁ , Amanda K Y Ngai ₁ , Vincent L Yuen ₁ , Nelson Kei ₁₅ , Timothy Y Y Lai ₁ , Hao Chen _{14,

16} , Clement C Tham _{1,

4} , Pheng-Ann Heng _{17,

18} , Carol Y Cheung ₁₉

Affiliation

Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
Zhejiang Lab, Hangzhou, Zhejiang, China.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
Hong Kong Eye Hospital, Hong Kong SAR, China.
Tsinghua Medicine, Tsinghua University, Beijing, China.
School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.
Department of Ophthalmology, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia.
Giridhar Eye Institute, Cochin, India.
Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India.
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
Institute of Medical Intelligence and XR, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China

Aims To develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices. Methods We retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively. Results In the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885–0.976) and 0.906 (0.863–0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730–0.934) to 0.930 (0.906–0.953) and 0.891 (0.836–0.945) to 0.962 (0.918–1.000), respectively. Conclusions Our models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow. No data are available.

更新日期：2024-10-22

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