ArticleBJS open2024
Parathyroid gland identification and angiography classification using simple machine learning methods.
Article in BJS open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Vascularity Assessment of Parathyroid Glands Using Low-Dose ICG and Probe-Based Fluorescence Detection.Annals of surgical oncology · 2026Article
- Research status and frontier trends of near-infrared autofluorescence for parathyroid identification and protection: a bibliometric analysis from 2006 to 2026.Gland surgery · 2026Article
- Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis.Scientific reports · 2026Article
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- AUGUR-AI: a real-time artificial intelligence interpreter for indocyanine green fluorescence angiography in colorectal surgery.Surgical endoscopy · 2025Article
- Impact of parathyroid gland classification on hypoparathyroidism following total thyroidectomy with central neck dissection for differentiated thyroid cancer.Annals of medicine · 2025Article
- Intraoperative applications of artificial intelligence for augmented parathyroid gland recognition: a narrative review.Gland surgery · 2025Review
- AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2025Observational
- VALUE OF THE INDOCYANINE GREEN FLUORESCENCE SCORE IN PREDICTING POSTOPERATIVE HYPOPARATHYROIDISM IN THYROID SURGERY - A PROSPECTIVE OBSERVATIONAL STUDY.Acta endocrinologica (Bucharest, Romania : 2005)Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNear-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.
methodsNear-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. Computation (MATLAB, Mathworks, Ireland) included segmentation-identification of the parathyroid gland (by autofluorescence), image stabilization (by linear translation) and adjusted time-fluorescence intensity profile generation. Relative upslope and maximum intensity ratios then trained a simple logistic regression model based on expert interpretation and outcome (including hypoparathyroidism), with subsequent unseen testing for validation.
resultsThe model was trained on 37 patient videos (45 glands, 29 judged well perfused by parathyroid gland angiography experts), achieving feature data separation with 100% accuracy, and tested on 22 unseen videos (27 glands, 15 judged well perfused), including four in real time. Segmentation-guided parathyroid gland detection correctly identified all parathyroid glands during unseen testing along with three additional non-parathyroid gland regions (90% positive predictive value). Subsequent time-fluorescence intensity profile extraction with vitality prediction was shown feasible in all cases within 5 min, with a 96.3% model accuracy (sensitivity and specificity were 93.3 and 100% respectively) when compared with expert judgement.
conclusionAutomatic parathyroid gland perfusion quantification using simple machine learning computational methods discriminates parathyroid gland perfusion in concordance with expert surgeon interpretation, providing a means for near-infrared indocyanine green-parathyroid gland signal evaluation.
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