Parathyroid gland identification and angiography classification using simple machine learning methods.

Background: Near-infrared indocyanine green angiography allows experienced surgeons to reliably evaluate parathyroid gland vitality during thyroid and parathyroid operations in order to predict postoperative function. To facilitate equal performance between surgeons, we developed an automatic computational quantification method using computer vision that portrays expert interpretation of visualized parathyroid gland near-infrared indocyanine green angiographic fluorescence signals.

Methods: Near-infrared indocyanine green-parathyroid gland angiography video recordings (Fluobeam® LX, Fluoptics, Grenoble-part of Getinge-Göteborg) from patients undergoing endocrine cervical surgery in a high-volume unit were used for model development.