Suri Jasjit S, Agarwal Sushant, Chabert Gian Luca, Carriero Alessandro, Paschè Alessio, Danna Pietro S C, Saba Luca, Mehmedović Armin, Faa Gavino, Singh Inder M, Turk Monika, Chadha Paramjit S, Johri Amer M, Khanna Narendra N, Mavrogeni Sophie, Laird John R, Pareek Gyan, Miner Martin, Sobel David W, Balestrieri Antonella, Sfikakis Petros P, Tsoulfas George, Protogerou Athanasios D, Misra Durga Prasanna, Agarwal Vikas, Kitas George D, Teji Jagjit S, Al-Maini Mustafa, Dhanjil Surinder K, Nicolaides Andrew, Sharma Aditya, Rathore Vijay, Fatemi Mostafa, Alizad Azra, Krishnan Pudukode R, Nagy Ferenc, Ruzsa Zoltan, Fouda Mostafa M, Naidu Subbaram, Viskovic Klaudija, Kalra Mannudeep K
Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA.
Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA.
Diagnostics (Basel). 2022 Jun 16;12(6):1482. doi: 10.3390/diagnostics12061482.
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.
先前的新冠病毒肺炎肺部诊断系统既缺乏科学验证，也缺乏可解释人工智能（AI）在理解病变定位方面的作用。本研究提出了一种基于云的可解释AI，即使用四种类激活映射（CAM）模型的“COVLIAS 2.0-cXAI”系统。方法：我们的队列由来自两个来源的约6000张CT切片组成（克罗地亚，80例新冠病毒肺炎患者；意大利，15例对照患者）。COVLIAS 2.0-cXAI设计包括三个阶段：（i）使用混合深度学习ResNet-UNet模型通过自动调整亨氏单位、超参数优化以及并行和分布式训练进行自动肺部分割，（ii）使用三种DenseNet（DN）模型（DN-121、DN-169、DN-201）进行分类，（iii）使用四种CAM可视化技术进行验证：梯度加权类激活映射（Grad-CAM）、Grad-CAM++、分数加权CAM（Score-CAM）和FasterScore-CAM。COVLIAS 2.0-cXAI由三名训练有素的资深放射科医生对其稳定性和可靠性进行了验证。还对三名放射科医生的评分进行了弗里德曼检验。结果：ResNet-UNet分割模型的骰子相似度为0.96，杰卡德指数为0.93，相关系数为0.99，品质因数为95.99%，而三种DN网络（DN-121、DN-169和DN-201）的分类器准确率分别为98%、98%和99%，在使用50个轮次时损失分别约为0.003、0.0025和0.002。所有三种DN模型的平均AUC为0.99（p<0.0001）。COVLIAS 2.0-cXAI显示，热图与金标准之间的平均对齐指数（MAI）在80%的扫描中得分为五分之四，为临床应用建立了该系统。结论：COVLIAS 2.0-cXAI成功展示了一种用于肺部CT扫描病变定位的基于云的可解释AI系统。
