A visual deep learning model to predict abnormal versus normal parathyroid glands using intraoperative autofluorescence signals.
J Surg Oncol
; 126(2): 263-267, 2022 Aug.
Article
in En
| MEDLINE
| ID: mdl-35416299
ABSTRACT
BACKGROUND:
Previous work demonstrated that abnormal versus normal parathyroid glands (PGs) exhibit different patterns of autofluorescence, with former appearing darker and more heterogenous. Our objective was to develop a visual artificial intelligence model using intraoperative autofluorescence signals to predict whether a PG is abnormal (hypersecreting and/or hypercellular) or normal before excision during surgical exploration for primary hyperparathyroidism.METHODS:
A total of 906 intraoperative parathyroid autofluorescence images of 303 patients undergoing parathyroidectomy/thyroidectomy were used to develop model. Autofluorescence image of each PG was uploaded into the visual artificial intelligence platform as abnormal or normal. For deep learning, randomly chosen 80% of data was used for training, 10% for testing, 10% for validation. The area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), recall (sensitivity), and precision (positive predictive value) of the model were calculated.RESULTS:
AUROC and AUPRC of the model to predict normal and abnormal PGs were 0.90 and 0.93, respectively. Recall and precision of the model were 89% each.CONCLUSION:
Visual artificial intelligence platforms may be used to compare the autofluorescence signal of a given parathyroid gland against a large database. This may be a new adjunctive tool for intraoperative assessment of parathyroid glands during surgical exploration for primary hyperparathyroidism.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Hyperparathyroidism, Primary
/
Deep Learning
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
J Surg Oncol
Year:
2022
Document type:
Article
Affiliation country: