Vasseneix Caroline, Nusinovici Simon, Xu Xinxing, Hwang Jeong-Min, Hamann Steffen, Chen John J, Loo Jing Liang, Milea Leonard, Tan Kenneth B K, Ting Daniel S W, Liu Yong, Newman Nancy J, Biousse Valerie, Wong Tien Ying, Milea Dan, Najjar Raymond P
Visual Neuroscience Group (CV, SN, DT, TYW, DM, RPN), Singapore Eye Research Institute, Singapore; Duke NUS Medical School (DT, TYW, DM, RPN), National University of Singapore, Singapore; Institute of High Performance Computing (XX, YL), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Ophthalmology (J-MH), Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Korea (the Republic of); Department of Ophthalmology (SH), Rigshospitalet, University of Copenhagen, Kobenhavn, Denmark ; Departments of Ophthalmology and Neurology (JJC), Mayo Clinic Rochester, Minnesota; Singapore National Eye Centre (JLL, DT, TYW, DM), Singapore; Berkeley University (LM), Berkeley, California; Department of Emergency Medicine (KT), Singapore General Hospital, Singapore; Departments of Ophthalmology, Neurology and Neurological Surgery (NJN, VB), Emory University School of Medicine, Atlanta, Georgia; and Department of Ophthalmology (RPN), Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
J Neuroophthalmol. 2023 Jun 1;43(2):159-167. doi: 10.1097/WNO.0000000000001800. Epub 2023 Feb 1.
The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown.
In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images.
With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively.
The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.
对于头痛、高血压或有任何神经症状的患者，对视神经乳头（视盘）进行检查是必不可少的，但在普通诊所中，这种检查很少进行或做得很差。我们最近开发了一种基于人工智能深度学习系统的脑和视神经研究（BONSAI-DLS），它能够在数字眼底照片上准确检测包括视乳头水肿（由于颅内压升高引起的肿胀）在内的视盘异常，其分类性能与神经眼科专家相当，但与一线临床医生相比，其性能仍不清楚。
在这项国际横断面多中心研究中，在14341张眼底照片上训练的DLS，对从454例患者中回顾性收集的800张照片（400张正常视盘、201张视乳头水肿和199张其他异常）的便利样本进行测试，这些照片由转诊的神经眼科专家提供了可靠的真实诊断。计算BONSAI-DLS的受试者操作特征曲线下面积。将该算法的错误率、准确性、敏感性和特异性与30名有或没有眼科培训的临床医生（6名普通眼科医生、6名验光师、6名神经科医生、6名内科医生、6名急诊科医生）对同一测试图像集进行分级的结果进行比较。
DLS的错误率为15.3%，在视盘外观的总体分类方面优于所有临床医生（普通眼科医生、验光师、神经科医生、内科医生和急诊科医生的平均错误率分别为24.4%、24.8%、38.2%、44.8%、47.9%）。对于视乳头水肿、正常和其他视盘异常的分类，DLS的准确率分别显著高于100%、86.7%和93.3%的临床医生（n = 30）。
BONSAI-DLS在眼底照片上对视盘进行分类的性能优于有或没有眼科培训的临床医生。经过训练的DLS可能为来自各种临床环境的临床医生提供有价值的诊断辅助，用于筛查伴有潜在视力或生命威胁性神经疾病的视盘异常。
