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Wong CYT, Antaki F, Woodward-Court P, Ong AY, Keane PA. The role of saliency maps in enhancing ophthalmologists' trust in artificial intelligence models. Asia Pac J Ophthalmol (Phila) 2024:100087. [PMID: 39069106 DOI: 10.1016/j.apjo.2024.100087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024] Open
Abstract
PURPOSE Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visualising the important features responsible for predictions. This ultimately improves interpretability and confidence. In this work, we review the use case for SMs, exploring their impact on clinicians' understanding and trust in AI models. We use the following ophthalmic conditions as examples: (1) glaucoma, (2) myopia, (3) age-related macular degeneration, and (4) diabetic retinopathy. METHOD A multi-field search on MEDLINE, Embase, and Web of Science was conducted using specific keywords. Only studies on the use of SMs in glaucoma, myopia, AMD, or DR were considered for inclusion. RESULTS Findings reveal that SMs are often used to validate AI models and advocate for their adoption, potentially leading to biased claims. Overlooking the technical limitations of SMs, and the conductance of superficial assessments of their quality and relevance, was discerned. Uncertainties persist regarding the role of saliency maps in building trust in AI. It is crucial to enhance understanding of SMs' technical constraints and improve evaluation of their quality, impact, and suitability for specific tasks. Establishing a standardised framework for selecting and assessing SMs, as well as exploring their relationship with other reliability sources (e.g. safety and generalisability), is essential for enhancing clinicians' trust in AI. CONCLUSION We conclude that SMs are not beneficial for interpretability and trust-building purposes in their current forms. Instead, SMs may confer benefits to model debugging, model performance enhancement, and hypothesis testing (e.g. novel biomarkers).
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Affiliation(s)
| | - Fares Antaki
- Institute of Ophthalmology, University College London, London, United Kingdom
| | | | - Ariel Yuhan Ong
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, United Kingdom.
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Zhou Z, Zheng Y, Zhou X, Yu J, Rong S. Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network. BMC Ophthalmol 2024; 24:98. [PMID: 38438876 PMCID: PMC10910696 DOI: 10.1186/s12886-024-03376-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/28/2024] [Indexed: 03/06/2024] Open
Abstract
Image segmentation is a fundamental task in deep learning, which is able to analyse the essence of the images for further development. However, for the supervised learning segmentation method, collecting pixel-level labels is very time-consuming and labour-intensive. In the medical image processing area for optic disc and cup segmentation, we consider there are two challenging problems that remain unsolved. One is how to design an efficient network to capture the global field of the medical image and execute fast in real applications. The other is how to train the deep segmentation network using a few training data due to some medical privacy issues. In this paper, to conquer such issues, we first design a novel attention-aware segmentation model equipped with the multi-scale attention module in the pyramid structure-like encoder-decoder network, which can efficiently learn the global semantics and the long-range dependencies of the input images. Furthermore, we also inject the prior knowledge that the optic cup lies inside the optic disc by a novel loss function. Then, we propose a self-supervised contrastive learning method for optic disc and cup segmentation. The unsupervised feature representation is learned by matching an encoded query to a dictionary of encoded keys using a contrastive technique. Finetuning the pre-trained model using the proposed loss function can help achieve good performance for the task. To validate the effectiveness of the proposed method, extensive systemic evaluations on different public challenging optic disc and cup benchmarks, including DRISHTI-GS and REFUGE datasets demonstrate the superiority of the proposed method, which can achieve new state-of-the-art performance approaching 0.9801 and 0.9087 F1 score respectively while gaining 0.9657 D C disc and 0.8976 D C cup . The code will be made publicly available.
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Affiliation(s)
- Zhiwang Zhou
- Institute for Advanced Study, Nanchang University, Nanchang, 330031, China.
| | - Yuanchang Zheng
- Institute for Advanced Study, Nanchang University, Nanchang, 330031, China
- Institute of Science and Technology, Waseda University, Tokyo, 63-8001, Japan
| | - Xiaoyu Zhou
- School of Transportation Engineering, Tongji University, Shanghai, 200000, China
| | - Jie Yu
- School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, 300000, China
| | - Shangjie Rong
- School of Mathematical Sciences, Xiamen University, Xiamen, 361000, China
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Nam Y, Kim J, Kim K, Park KA, Kang M, Cho BH, Oh SY, Kee C, Han J, Lee GI, Kang MC, Lee D, Choi Y, Yun HJ, Park H, Kim J, Cho SJ, Chang DK. Deep learning-based optic disc classification is affected by optic-disc tilt. Sci Rep 2024; 14:498. [PMID: 38177229 PMCID: PMC10767025 DOI: 10.1038/s41598-023-50256-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024] Open
Abstract
We aimed to determine the effect of optic disc tilt on deep learning-based optic disc classification. A total of 2507 fundus photographs were acquired from 2236 eyes of 1809 subjects (mean age of 46 years; 53% men). Among all photographs, 1010 (40.3%) had tilted optic discs. Image annotation was performed to label pathologic changes of the optic disc (normal, glaucomatous optic disc changes, disc swelling, and disc pallor). Deep learning-based classification modeling was implemented to develop optic-disc appearance classification models with the photographs of all subjects and those with and without tilted optic discs. Regardless of deep learning algorithms, the classification models showed better overall performance when developed based on data from subjects with non-tilted discs (AUC, 0.988 ± 0.002, 0.991 ± 0.003, and 0.986 ± 0.003 for VGG16, VGG19, and DenseNet121, respectively) than when developed based on data with tilted discs (AUC, 0.924 ± 0.046, 0.928 ± 0.017, and 0.935 ± 0.008). In classification of each pathologic change, non-tilted disc models had better sensitivity and specificity than the tilted disc models. The optic disc appearance classification models developed based all-subject data demonstrated lower accuracy in patients with the appearance of tilted discs than in those with non-tilted discs. Our findings suggested the need to identify and adjust for the effect of optic disc tilt on the optic disc classification algorithm in future development.
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Affiliation(s)
- Youngwoo Nam
- Medical AI Research Center, Institute of Smart Healthcare, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Joonhyoung Kim
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyung-Ah Park
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Mira Kang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
- Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Digital Innovation Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Baek Hwan Cho
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongam, Republic of Korea
| | - Sei Yeul Oh
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Changwon Kee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jongchul Han
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Ga-In Lee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Min Chae Kang
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Dongyoung Lee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yeeun Choi
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jee Yun
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hansol Park
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jiho Kim
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Soo Jin Cho
- Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Dong Kyung Chang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Division of Gastroenterology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Guo X, Li R, Lu X, Zhang X, Wu Q, Tian Q, Guo B, Tang G, Xu J, Feng J, Zhao L, Ling S, Dong Z, Song J, Bi H. Quantization of Optic Disc Characteristics in Young Adults Based on Artificial Intelligence. Curr Eye Res 2023; 48:1068-1077. [PMID: 37555317 DOI: 10.1080/02713683.2023.2244700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/10/2023]
Abstract
PURPOSE This study aimed to automatically and quantitatively analyse the characteristics of the optic disc by applying artificial intelligence (AI) to fundus images. METHODS A total of 1084 undergraduates were recruited in this cross-sectional study. The optic disc area, cup-to-disc ratio (C/D), optic disc tilt, and the area, width, and height of peripapillary atrophy (PPA) were automatically and quantitatively detected using AI. Based on axial length (AL), participants were divided into five groups: Group 1 (AL ≤ 23 mm); Group 2 (23 mm < AL≤ 24 mm); Group 3 (24 mm < AL≤ 25 mm); Group 4 (25 mm < AL< 26 mm) and Group 5 (AL ≥ 26 mm). Relationships between ocular parameters and optic disc characteristics were analysed. RESULT A total of 999 undergraduates were included in the analysis. The prevalence of optic disc tilting and PPA were 47.1% and 92.5%, respectively, and increased with the severity of myopia. The mean optic disc area, PPA area, C/D, and optic disc tilt ratio were 1.97 ± 0.46 mm2, 0.84 ± 0.59 mm2, 0.18 ± 0.07, and 0.81 ± 0.08, respectively. In Group 5, the average optic disc area (1.84 ± 0.41 mm2) and optic disc tilt ratio (0.79 ± 0.08) were significantly smaller and the PPA area (1.12 ± 0.61 mm2) was significantly larger than those in the other groups. AL was negatively correlated with optic disc area and optic disc tilt ratio (r=-0.271, -0.219; both p < 0.001) and positively correlated with PPA area, width, and height (r = 0.421, 0.426, 0.345; all p < 0.01). A greater AL (β = 0.284, p < 0.01) and a smaller optic disc tilt ratio (β=-0.516, p < 0.01) were related to a larger PPA area. CONCLUSION The characteristics of the optic disc can be feasibly and efficiently extracted using AI. The quantization of the optic disc might provide new indicators for clinicians to evaluate the degree of myopia.
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Affiliation(s)
- Xiaoxiao Guo
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Runkuan Li
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Xiuzhen Lu
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Xiuyan Zhang
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Qiuxin Wu
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Qingmei Tian
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Bin Guo
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Guodong Tang
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Jing Xu
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Jiaojiao Feng
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Lili Zhao
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Saiguang Ling
- EVision Technology (Beijing) Co., Ltd, Beijing, P. R. China
| | - Zhou Dong
- EVision Technology (Beijing) Co., Ltd, Beijing, P. R. China
| | - Jike Song
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Hongsheng Bi
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
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Park S, Kim JH, Cha YK, Chung MJ, Woo JH, Park S. Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT. Diagnostics (Basel) 2023; 13:2953. [PMID: 37761320 PMCID: PMC10528867 DOI: 10.3390/diagnostics13182953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.
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Affiliation(s)
- Soyoung Park
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea; (S.P.); (S.P.)
| | - Jong Hee Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Yoon Ki Cha
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Jung Han Woo
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Subin Park
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea; (S.P.); (S.P.)
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You S, Hwan Cho B, Shon YM, Seo DW, Kim IY. Semi-supervised automatic seizure detection using personalized anomaly detecting variational autoencoder with behind-the-ear EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106542. [PMID: 34839270 DOI: 10.1016/j.cmpb.2021.106542] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/14/2021] [Accepted: 11/15/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is one of the most common neurologic diseases worldwide, and 30% of the patients live with uncontrolled seizures. For the safety of patients with epilepsy, an automatic seizure detection algorithm for continuous seizure monitoring in daily life is important to reduce risks related to seizures, including sudden unexpected death. Previous researchers applied machine learning to detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to identify. Furthermore, the imbalance problem due to the small proportion of ictal events caused poor prediction performance in supervised learning approaches. This study aimed to present a personalized deep learning-based anomaly detection algorithm for seizure monitoring with behind-the-ear electroencephalogram (EEG) signals. METHODS We collected behind-the-ear EEG signals from 16 patients with epilepsy in the hospital and used them to develop and evaluate seizure detection algorithms. We modified the variational autoencoder network to learn the latent representation of normal EEG signals and performed seizure detection by measuring the anomalies in EEG signals using the trained network. To personalize the algorithm, we also proposed a method to calibrate the anomaly score for each patient by comparing the representations in the latent space. RESULTS Our proposed algorithm showed a sensitivity of 90.4% with a false alarm rate of 0.83 per hour without personal calibration. On the other hand, the one-class support vector machine only showed a sensitivity of 84.6% with a false alarm rate of 2.17 per hour. Furthermore, our proposed model with personal calibration achieved 94.2% sensitivity with a false alarm rate of 0.29 while detecting 49 of 52 ictal events. CONCLUSIONS We proposed a novel seizure detection algorithm with behind-the-ear EEG signals via semi-supervised learning of an anomaly detecting variational autoencoder and personalization method of anomaly scoring by comparing latent representations. Our approach achieved improved seizure detection with high sensitivity and a lower false alarm rate.
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Affiliation(s)
- Sungmin You
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Baek Hwan Cho
- Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea
| | - Young-Min Shon
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Dae-Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
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Choi KJ, Choi JE, Roh HC, Eun JS, Kim JM, Shin YK, Kang MC, Chung JK, Lee C, Lee D, Kang SW, Cho BH, Kim SJ. Deep learning models for screening of high myopia using optical coherence tomography. Sci Rep 2021; 11:21663. [PMID: 34737335 PMCID: PMC8568935 DOI: 10.1038/s41598-021-00622-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/13/2021] [Indexed: 12/02/2022] Open
Abstract
This study aimed to validate and evaluate deep learning (DL) models for screening of high myopia using spectral-domain optical coherence tomography (OCT). This retrospective cross-sectional study included 690 eyes in 492 patients with OCT images and axial length measurement. Eyes were divided into three groups based on axial length: a “normal group,” a “high myopia group,” and an “other retinal disease” group. The researchers trained and validated three DL models to classify the three groups based on horizontal and vertical OCT images of the 600 eyes. For evaluation, OCT images of 90 eyes were used. Diagnostic agreements of human doctors and DL models were analyzed. The area under the receiver operating characteristic curve of the three DL models was evaluated. Absolute agreement of retina specialists was 99.11% (range: 97.78–100%). Absolute agreement of the DL models with multiple-column model was 100.0% (ResNet 50), 90.0% (Inception V3), and 72.22% (VGG 16). Areas under the receiver operating characteristic curves of the DL models with multiple-column model were 0.99 (ResNet 50), 0.97 (Inception V3), and 0.86 (VGG 16). The DL model based on ResNet 50 showed comparable diagnostic performance with retinal specialists. The DL model using OCT images demonstrated reliable diagnostic performance to identify high myopia.
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Affiliation(s)
- Kyung Jun Choi
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jung Eun Choi
- Medical AI Research Center, Samsung Medical Center, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hyeon Cheol Roh
- Department of Ophthalmology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
| | - Jun Soo Eun
- Department of Ophthalmology, Gil Medical Center, Gachon University, Incheon, Republic of Korea
| | | | - Yong Kyun Shin
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Min Chae Kang
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Joon Kyo Chung
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Chaeyeon Lee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Dongyoung Lee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Se Woong Kang
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Baek Hwan Cho
- Medical AI Research Center, Samsung Medical Center, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea.
| | - Sang Jin Kim
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Jothi Balaji J, Lakshminarayanan V. ODTiD: Optic Nerve Head SD-OCT Image Dataset. Clin Ophthalmol 2021; 15:4239-4245. [PMID: 34707343 PMCID: PMC8544271 DOI: 10.2147/opth.s337174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/11/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction Optic disc tilt (ODT) or tilted optic disc is a common finding in the general population. It is due to anomalous development caused by the malclosure of the embryonic optic fissure. ODT is commonly associated with high myopia as well as other conditions. In recent days, the common method to image the optic disc (OD) is by optical coherence tomography (OCT). To the best of our knowledge, there are no datasets of ODT available in the public domain. This dataset aims to make open access raw ODT OCT images to test out new image processing segmentation algorithms. Methods This dataset of ODT images contains both horizontal and vertical cross-sectional images obtained using spectral-domain optical coherence tomography (SD-OCT, Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA). The optic disc cube 200×200 program was used and all the images are aligned with the center of the optic nerve head. This dataset includes images from both clinically normal (20 eyes) and myopic subjects (101 eyes). Results The dataset consists of clear (121) and manually marked (121) images resulting in a total of 242 images. The age distribution for all subjects combined is 27.24 ± 9.28 (range, 11.0-69.0) years. For normal subjects mean ± SD age distribution is 32.40 ± 17.23 years. Similarly, the myopia age distribution is 26.22 ± 6.37 years. Ground truth images, ie, manually segmented by a clinical expert are provided along with other meta-data includes age, gender, laterality, refractive error classification, spherical equivalent (SE), best-corrected visual acuity (BCVA), intraocular pressure (IOP), and axial length (AXL). Conclusion This open, public database is online at the ICPSR website of the University of Michigan. The dataset can be used to test and validate newly developed automated segmentation algorithms.
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Affiliation(s)
| | - Vasudevan Lakshminarayanan
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
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Aoyama Y, Maruko I, Kawano T, Yokoyama T, Ogawa Y, Maruko R, Iida T. Diagnosis of central serous chorioretinopathy by deep learning analysis of en face images of choroidal vasculature: A pilot study. PLoS One 2021; 16:e0244469. [PMID: 34143775 PMCID: PMC8213187 DOI: 10.1371/journal.pone.0244469] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 05/12/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose To diagnose central serous chorioretinopathy (CSC) by deep learning (DL) analyses of en face images of the choroidal vasculature obtained by optical coherence tomography (OCT) and to analyze the regions of interest for the DL from heatmaps. Methods One-hundred eyes were studied; 53 eyes with CSC and 47 normal eyes. Volume scans of 12×12 mm square were obtained at the same time as the OCT angiographic (OCTA) scans (Plex Elite 9000 Swept-Source OCT®, Zeiss). High-quality en face images of the choroidal vasculature of the segmentation slab of one-half of the subfoveal choroidal thickness were created for the analyses. The 100 en face images were divided into 80 for training and 20 for validation. Thus, we divided it into five groups of 20 eyes each, trained the remaining 80 eyes in each group, and then calculated the correct answer rate for each group by validation with 20 eyes. The Neural Network Console (NNC) developed by Sony and the Keras-Tensorflow backend developed by Google were used as the software for the classification with 16 layers of convolutional neural networks. The active region of the heatmap based on the feature quantity extracted by DL was also evaluated as the percentages with gradient-weighted class activation mapping implemented in Keras. Results The mean accuracy rate of the validation was 95% for NNC and 88% for Keras. This difference was not significant (P >0.1). The mean active region in the heatmap image was 12.5% in CSC eyes which was significantly lower than the 79.8% in normal eyes (P<0.01). Conclusions CSC can be automatically diagnosed by DL with high accuracy from en face images of the choroidal vasculature with different programs, convolutional layer structures, and small data sets. Heatmap analyses showed that the DL focused on the area occupied by the choroidal vessels and their uniformity. We conclude that DL can help in the diagnosis of CSC.
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Affiliation(s)
- Yukihiro Aoyama
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Ichiro Maruko
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Taizo Kawano
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Tatsuro Yokoyama
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Yuki Ogawa
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Ruka Maruko
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Tomohiro Iida
- Department of Ophthalmology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
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Shi Z, Wang T, Huang Z, Xie F, Song G. A method for the automatic detection of myopia in Optos fundus images based on deep learning. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3460. [PMID: 33773080 DOI: 10.1002/cnm.3460] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 03/08/2021] [Accepted: 03/20/2021] [Indexed: 06/12/2023]
Abstract
Myopia detection is significant for preventing irreversible visual impairment and diagnosing myopic retinopathy. To improve the detection efficiency and accuracy, a Myopia Detection Network (MDNet) that combines the advantages of dense connection and Residual Squeeze-and-Excitation attention is proposed in this paper to automatically detect myopia in Optos fundus images. First, an automatic optic disc recognition method is applied to extract the Regions of Interest and remove the noise disturbances; then, data augmentation techniques are implemented to enlarge the data set and prevent overfitting; moreover, an MDNet composed of Attention Dense blocks is constructed to detect myopia in Optos fundus images. The results show that the Mean Absolute Error of the Spherical Equivalent detected by this network can reach 1.1150 D (diopter), which verifies the feasibility and applicability of this method for the automatic detection of myopia in Optos fundus images.
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Affiliation(s)
- Zhengjin Shi
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China
| | - Tianyu Wang
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China
| | - Zheng Huang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Feng Xie
- School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China
| | - Guoli Song
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
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Mitchell WG, Dee EC, Celi LA. Generalisability through local validation: overcoming barriers due to data disparity in healthcare. BMC Ophthalmol 2021; 21:228. [PMID: 34020592 PMCID: PMC8138973 DOI: 10.1186/s12886-021-01992-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/14/2021] [Indexed: 12/23/2022] Open
Abstract
Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity. Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation. The importance of external ML model validation on diverse populations should be highlighted where possible – especially for models built with single-centre data.
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Affiliation(s)
- William Greig Mitchell
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, MA, USA.,Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - Leo Anthony Celi
- Harvard TH Chan School of Public Health, Boston, MA, USA. .,Harvard Medical School, Boston, MA, USA. .,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Centre, Boston, MA, USA.
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