Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
SIOS Technology, Inc., Tokyo, Japan.
J Gynecol Oncol. 2024 May;35(3):e24. doi: 10.3802/jgo.2024.35.e24. Epub 2023 Dec 7.
Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources.
The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas.
Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity.
Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.
磁共振成像（MRI）在术前子宫肉瘤的诊断中具有较高的效率，但也可能存在误诊。本研究开发了一种新的人工智能（AI）系统，以克服需要专家手动处理数据集和大量计算机资源的局限性。
该 AI 系统包括肿瘤图像滤波器，用于提取包含肿瘤的 MRI 切片；以及肉瘤评估器，用于诊断子宫肉瘤。我们使用了 15 种 MRI 患者序列，通过 8 个交叉验证集训练肿瘤滤波器和肉瘤评估器所使用的深度神经网络（DNN）模型。我们使用 9 个 DNN 模型的集成预测技术实现了肿瘤滤波器和肉瘤评估器。开发了 10 个肿瘤滤波器和肉瘤评估器集，以评估波动准确性。最后，AutoDiag-AI 用于评估新的验证数据集，其中包括 8 例肉瘤和 24 例平滑肌瘤。
肿瘤图像滤波器和肉瘤评估器的准确率分别为 92.68%和 90.50%。使用原始数据集的 AutoDiag-AI 准确率为 89.32%，灵敏度为 90.47%，特异性为 88.95%；而使用新验证数据集的 AutoDiag-AI 准确率为 92.44%，灵敏度为 92.25%，特异性为 92.50%。
我们新建立的 AI 系统能够自动从 MRI 图像中提取肿瘤部位，并在无需人工干预的情况下诊断为子宫肉瘤。其准确率可与放射科医生相媲美。随着进一步验证，该系统可应用于其他疾病的诊断。进一步提高系统的准确性可能使其在未来能够得到临床应用。
