未经证实:在OCT体积扫描中手动识别地理萎缩(GA)的存在和位置可能是具有挑战性和耗时的。这项研究同时开发了一种深度学习模型(1)从OCT体积扫描中自动检测GA的存在或不存在,以及(2)通过展示哪些B扫描区域显示GA来提供可解释性。
未经批准:Med-XAI-Net,开发了一种可解释的深度学习模型,用于仅使用体积扫描标签从OCT体积扫描中检测GA的存在或不存在,以及解释最相关的B扫描和B扫描区域。
UNASISIGNED:311名参与者进行了一千二百八十四个OCT体积扫描(每个包含100个B扫描),包括321卷与GA和963卷没有GA。
UNASSIGNED:Med-XAI-Net通过使用区域注意模块在每个B扫描中定位最相关的区域来模拟人类诊断过程,然后是图像注意模块,以选择最相关的B扫描来分类每个OCT体积扫描中的GA存在或不存在。Med-XAI-Net进行了培训和测试(80%和20%的参与者,分别)使用来自人类专家分级者的黄金标准体积扫描标签。
未经评估:准确性,接收器工作特性(ROC)曲线下面积,F1得分,灵敏度,和特异性。
UNASSIGNED:在检测GA存在或不存在时,Med-XAI-Net获得了卓越的性能(91.5%,93.5%,82.3%,82.8%,准确率为94.6%,ROC曲线下的面积,F1得分,灵敏度,和特异性,分别)到其他两种最先进的深度学习方法。眼科医生的表现仅将Med-XAI-Net选择的5个B扫描进行分级(95.7%,95.4%,91.2%,100%,分别)几乎与眼科医生对所有体积扫描进行分级(96.0%,95.7%,91.8%,100%,分别)。即使在1个B扫描中只对1个区域进行分级,眼科医生表现出中等高的表现(89.0%,87.4%,77.6%,100%,分别)。
UNASSIGNED:尽管仅在体积扫描级别的训练期间使用了地面实况标签,Med-XAI-Net可有效地在B扫描中定位GA,并在每个体积扫描中选择相关的B扫描以进行GA诊断。这些结果说明了Med-XAI-Net在解释哪些区域和B扫描有助于体积扫描中的GA检测方面的优势。
UNASSIGNED: Manually identifying geographic atrophy (GA) presence and location on OCT volume scans can be challenging and time consuming. This study developed a deep learning model simultaneously (1) to perform automated detection of GA presence or absence from OCT volume scans and (2) to provide interpretability by demonstrating which regions of which B-scans show GA.
UNASSIGNED: Med-XAI-Net, an interpretable deep learning model was developed to detect GA presence or absence from OCT volume scans using only volume scan labels, as well as to interpret the most relevant B-scans and B-scan regions.
UNASSIGNED: One thousand two hundred eighty-four OCT volume scans (each containing 100 B-scans) from 311 participants, including 321 volumes with GA and 963 volumes without GA.
UNASSIGNED: Med-XAI-Net simulates the human diagnostic process by using a region-attention module to locate the most relevant region in each B-scan, followed by an image-attention module to select the most relevant B-scans for classifying GA presence or absence in each OCT volume scan. Med-XAI-Net was trained and tested (80% and 20% participants, respectively) using gold standard volume scan labels from human expert graders.
UNASSIGNED: Accuracy, area under the receiver operating characteristic (ROC) curve, F1 score, sensitivity, and specificity.
UNASSIGNED: In the detection of GA presence or absence, Med-XAI-Net obtained superior performance (91.5%, 93.5%, 82.3%, 82.8%, and 94.6% on accuracy, area under the ROC curve, F1 score, sensitivity, and specificity, respectively) to that of 2 other state-of-the-art deep learning methods. The performance of ophthalmologists grading only the 5 B-scans selected by Med-XAI-Net as most relevant (95.7%, 95.4%, 91.2%, and 100%, respectively) was almost identical to that of ophthalmologists grading all volume scans (96.0%, 95.7%, 91.8%, and 100%, respectively). Even grading only 1 region in 1 B-scan, the ophthalmologists demonstrated moderately high performance (89.0%, 87.4%, 77.6%, and 100%, respectively).
UNASSIGNED: Despite using ground truth labels during training at the volume scan level only, Med-XAI-Net was effective in locating GA in B-scans and selecting relevant B-scans within each volume scan for GA diagnosis. These results illustrate the strengths of Med-XAI-Net in interpreting which regions and B-scans contribute to GA detection in the volume scan.