AI-powered imaging could transform how surgeons identify diseased thyroid tissue
Researchers have demonstrated that deep learning models can automatically distinguish healthy from diseased thyroid tissue in real-time during surgery with 90% accuracy. The breakthrough could reduce surgical complications and operating time by giving surgeons instant visual guidance—potentially lowering healthcare costs while improving patient outcomes.
Originaltitel: Diseased thyroid tissue classification in OCT images using deep learning: towards surgical decision support
<p>Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue. However, OCT images are difficult to interpret, thus, real-time automatic analysis could support the clinical decision-making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open-access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthews correlation coefficient (MCC) of 0.79 (accuracy = 0.90) for the normal-versus-abnormal classification. On the open-access datasets, the custom models achieved the best performance (MCC > 0.88, accuracy > 0.96). Results obtained for the normal-versus-abnormal classification suggest OCT, complemented with deep learning-based analysis, as a tool for real-time automatic diseased tissue identification in thyroid surgery.</p>