ArticleAnnals of medicine2024
Artificial intelligence-enhanced infrared thermography as a diagnostic tool for thyroid malignancy detection.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The role of infrared thermography in thyroid nodule detection and classification: a scoping review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Article
- Active Versus Passive Infrared Thermography for Skin Cancer Detection: A Diagnostic Accuracy Study.Cancers · 2026Article
- Investigation of thoracico-abdominal infrared thermal imaging characteristics in patients with cervical HR-HPV infection.Scientific reports · 2026Article
- Manual annotation based sentiment analysis of user feedback in health and wellness app reviews.Scientific reports · 2025Article
- Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications.Sensors (Basel, Switzerland) · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
introductionThyroid 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.
methodsWe 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.
resultsThe 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%.
conclusionsThe 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.
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