Computer-aided detection of Parathyroid glands using a dual-RGB/NIR imaging and deep learning technique

Parathyroid glands (PGs), small endocrine glands in the neck, control calcium levels in the body and are crucial to maintaining homeostasis. Accidental removal of or direct damage to healthy parathyroid glands during thyroid surgery may occur due to its small size and similar appearance to surrounding anatomical structures, potentially leading to postoperative hypocalcemia. Thus precise and quick detection of normal parathyroid glands in real-time during surgery can improve the surgical outcome. In this study, we introduce a deep learning system (YOLOv5) based on dual RGB/NIR imaging for Computer-aided detection (CADe) of PG with high accuracy. This model can effectively detect parathyroid glands in real-time as it also includes the confidence level, which can help surgeons make decisions. We tested a computer-aided detection (CADe) using the co-registered RGB/NIR camera and ex-vivo thyroid tissue specimen. The average precisions of models were significantly higher when trained by the dual-RGB/NIR (0.99) data than NIR (0.94) and RGB (0.96) data alone at a high confidence threshold (0.7). The proposed CADe may increase the parathyroid detection rates clinically.