Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Breast and Endocrine Surgery Unit, Department of Surgery, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Ann Med. 2024 Dec;56(1):2425826. doi: 10.1080/07853890.2024.2425826. Epub 2024 Nov 8.
Thyroid nodules are common, and investigation is crucial for excluding malignancy. Increased intranodular vascularity is frequently observed in malignant tumors, which can be detected through increased skin surface temperatures using noninvasive infrared thermography. We aimed to develop a diagnostic tool for thyroid cancer using infrared thermal images combined with an artificial intelligence (AI) algorithm.
We conducted a prospective cross-sectional study involving participants with thyroid nodules undergoing thyroid surgery. Infrared thermal images were collected using a thermal camera on the day prior to surgery. In combination with the final thyroid pathological reports, we utilized a machine learning model based on the pre-trained ResNet50V2 model, a convolutional neural network, to evaluate diagnostic accuracy for malignancy diagnosis.
The study included 98 participants, 58 with malignant thyroid nodules and 40 with benign thyroid nodules, as determined by pathological results. The AI-enhanced infrared thermal image analyses demonstrated good performance in distinguishing between benign and malignant thyroid nodules, achieving an accuracy of 75% and a sensitivity of 78%. These parameters were slightly lower than those of the AI-model predictor that integrated current practice using preoperative thyroid ultrasound findings and cytological results, yielding an accuracy of 81% and a sensitivity of 84%.
The infrared thermal images, assisted by an AI model, exhibit good performance in distinguishing thyroid malignancy from benign nodules. This imaging modality has great potential to be used as a noninvasive screening tool for adjunct evaluation of thyroid nodules.
甲状腺结节很常见，因此进行检查以排除恶性肿瘤至关重要。恶性肿瘤中常观察到结节内血管增多，可通过使用非侵入性的红外热成像技术检测到皮肤表面温度升高。我们旨在开发一种使用红外热图像结合人工智能（AI）算法的甲状腺癌诊断工具。
我们进行了一项前瞻性的横断面研究，纳入了接受甲状腺手术的甲状腺结节患者。在手术前一天使用热像仪采集红外热图像。结合最终的甲状腺病理报告，我们利用基于预训练的 ResNet50V2 模型的机器学习模型（卷积神经网络）评估对恶性肿瘤诊断的诊断准确性。
该研究共纳入 98 名参与者，其中 58 名患有恶性甲状腺结节，40 名患有良性甲状腺结节，这些结果是通过病理结果确定的。人工智能增强的红外热图像分析在区分良性和恶性甲状腺结节方面表现出良好的性能，准确性为 75%，敏感性为 78%。这些参数略低于整合术前甲状腺超声检查结果和细胞学结果的 AI 模型预测器的参数，其准确性为 81%，敏感性为 84%。
红外热图像在辅助 AI 模型区分甲状腺良恶性结节方面表现出良好的性能。这种成像方式很有潜力成为甲状腺结节辅助评估的非侵入性筛查工具。
