Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It results in a validation macro F1 score for predicting the presence of COVID-19 in the CT scans at 92.2% which is significantly above the baseline of 74%. It gives a macro F1 score for predicting the severity of COVID-19 on the validation set for task 2 as 67% which is above the baseline of 38%.

翻译：改进医学影像的自动化分析将为临床医生提供更多患者护理选择。2023年人工智能医学影像分析研讨会暨新冠肺炎诊断竞赛（AI-MIA-COV19D）为测试和优化基于CT扫描检测患者COVID-19存在与严重程度的机器学习方法提供了契机。本文介绍了Cov3d模型的第二版本，该深度学习模型曾提交至2022年竞赛。通过引入预处理步骤——分割CT扫描中的肺部区域并将输入裁剪至该区域——模型性能得到提升。在验证集上，预测CT扫描中COVID-19存在的宏F1分数达到92.2%，显著高于74%的基线水平；针对任务二中COVID-19严重程度预测的宏F1分数为67%，高于38%的基线值。