Ocular Disease Detection with Deep Learning (Fine-Grained Image Categorization) Applied to Ocular B-Scan Ultrasound Images.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Ophthalmology and Therapy Pub Date : 2024-10-01 Epub Date: 2024-08-11 DOI:10.1007/s40123-024-01009-7

Xin Ye, Shucheng He, Ruilong Dan, Shangchao Yang, Jiahao Xv, Yang Lu, Bole Wu, Congying Zhou, Han Xu, Jiafeng Yu, Wenbin Xie, Yaqi Wang, Lijun Shen

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引用次数: 0

Abstract

Introduction: The aim of this work is to develop a deep learning (DL) system for rapidly and accurately screening for intraocular tumor (IOT), retinal detachment (RD), vitreous hemorrhage (VH), and posterior scleral staphyloma (PSS) using ocular B-scan ultrasound images.

Methods: Ultrasound images from five clinically confirmed categories, including vitreous hemorrhage, retinal detachment, intraocular tumor, posterior scleral staphyloma, and normal eyes, were used to develop and evaluate a fine-grained classification system (the Dual-Path Lesion Attention Network, DPLA-Net). Images were derived from five centers scanned by different sonographers and divided into training, validation, and test sets in a ratio of 7:1:2. Two senior ophthalmologists and four junior ophthalmologists were recruited to evaluate the system's performance.

Results: This multi-center cross-sectional study was conducted in six hospitals in China. A total of 6054 ultrasound images were collected; 4758 images were used for the training and validation of the system, and 1296 images were used as a testing set. DPLA-Net achieved a mean accuracy of 0.943 in the testing set, and the area under the curve was 0.988 for IOT, 0.997 for RD, 0.994 for PSS, 0.988 for VH, and 0.993 for normal. With the help of DPLA-Net, the accuracy of the four junior ophthalmologists improved from 0.696 (95% confidence interval [CI] 0.684-0.707) to 0.919 (95% CI 0.912-0.926, p < 0.001), and the time used for classifying each image reduced from 16.84 ± 2.34 s to 10.09 ± 1.79 s.

Conclusions: The proposed DPLA-Net showed high accuracy for screening and classifying multiple ophthalmic diseases using B-scan ultrasound images across mutiple centers. Moreover, the system can promote the efficiency of classification by ophthalmologists.

Abstract Image

查看原文本刊更多论文

应用于眼部 B-Scan 超声波图像的深度学习（细粒度图像分类）眼部疾病检测。

导言：本研究旨在开发一种深度学习（DL）系统，利用眼部B扫描超声图像快速准确地筛查眼内肿瘤（IOT）、视网膜脱离（RD）、玻璃体出血（VH）和后巩膜葡萄胎（PSS）：方法：利用临床确诊的五类超声图像（包括玻璃体出血、视网膜脱离、眼内肿瘤、后巩膜葡萄状瘤和正常眼）来开发和评估细粒度分类系统（双路径病变注意网络，DPLA-Net）。图像来自五个中心，由不同的超声波技师扫描，并按 7:1:2 的比例分为训练集、验证集和测试集。招募了两名资深眼科医生和四名初级眼科医生来评估系统的性能：这项多中心横断面研究在中国六家医院进行。共收集了 6054 张超声图像，其中 4758 张用于系统的训练和验证，1296 张作为测试集。在测试集中，DPLA-Net 的平均准确率为 0.943，曲线下面积分别为：IOT 0.988、RD 0.997、PSS 0.994、VH 0.988 和正常 0.993。在 DPLA-Net 的帮助下，四名初级眼科医生的准确率从 0.696（95% 置信区间 [CI] 0.684-0.707）提高到 0.919（95% CI 0.912-0.926，p 结论：所提出的 DPLA-Net 在多个中心使用 B-scan 超声图像筛查多种眼科疾病并对其进行分类方面表现出较高的准确性。此外，该系统还能提高眼科医生的分类效率。

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来源期刊

Ophthalmology and Therapy OPHTHALMOLOGY-

CiteScore

4.20

自引率

3.00%

发文量

157

审稿时长

6 weeks

期刊介绍： Aims and Scope Ophthalmology and Therapy is an international, open access, peer-reviewed (single-blind), and rapid publication journal. The scope of the journal is broad and will consider all scientifically sound research from preclinical, clinical (all phases), observational, real-world, and health outcomes research around the use of ophthalmological therapies, devices, and surgical techniques. The journal is of interest to a broad audience of pharmaceutical and healthcare professionals and publishes original research, reviews, case reports/series, trial protocols and short communications such as commentaries and editorials. Ophthalmology and Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. 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