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Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.
- Source :
- Surgical Endoscopy & Other Interventional Techniques; Oct2024, Vol. 38 Issue 10, p5732-5745, 14p
- Publication Year :
- 2024
-
Abstract
- Background: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy. Methods: Video clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands. Results: 152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods. Conclusion: This AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model. [ABSTRACT FROM AUTHOR]
- Subjects :
- PARATHYROID gland surgery
PARATHYROID glands
GENERATIVE artificial intelligence
HYPOPARATHYROIDISM
STATISTICAL models
DIAGNOSTIC imaging
RESEARCH funding
THYROID gland tumors
PHOSPHORUS
RECEIVER operating characteristic curves
SURGICAL therapeutics
EVALUATION of medical care
DIAGNOSTIC errors
DESCRIPTIVE statistics
PARATHYROID hormone
CALCIUM
ARTIFICIAL neural networks
NEEDLE biopsy
DEEP learning
DIGITAL image processing
MACHINE learning
ACCURACY
STAINS & staining (Microscopy)
DATA analysis software
THYROIDECTOMY
VIDEO recording
Subjects
Details
- Language :
- English
- ISSN :
- 18666817
- Volume :
- 38
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Surgical Endoscopy & Other Interventional Techniques
- Publication Type :
- Academic Journal
- Accession number :
- 180131561
- Full Text :
- https://doi.org/10.1007/s00464-024-11115-z