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The Parathyroid Glands

The Parathyroid Glands

The two pairs of parathyroid glands embedded within the posterior surface of the thyroid gland are restricted by a dense capsule around them. These glands comprise two distinct cell populations—parathyroid oxyphil and parathyroid principal cells- pivotal in calcium homeostasis.
Oxyphil cells, whose functions remain elusive, emerge during late puberty, adding a layer of complexity to the parathyroid gland's intricacies. In contrast, principal parathyroid cells undertake a vital role by...

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Updated: Feb 20, 2026

Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging

Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging

Published on: August 17, 2022

Machine learning to identify multigland disease in primary hyperparathyroidism.

Joseph R Imbus¹, Reese W Randle¹, Susan C Pitt¹

¹Department of Surgery, University of Wisconsin, Madison, Wisconsin.

The Journal of Surgical Research

|October 29, 2017

Summary

Machine learning accurately predicts multigland disease (MGD) in primary hyperparathyroidism, improving surgical planning. This approach enhances early clinical evaluation, potentially reducing unnecessary preoperative imaging for MGD patients.

Keywords:

Clinical decision support Diagnosis Machine learning Multigland disease Parathyroid glands Primary hyperparathyroidism

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Last Updated: Feb 20, 2026

Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging

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Published on: August 17, 2022

Procurement of Parathyroid Glands from Living Donor Pigs and Ex Vivo Identification

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Published on: August 1, 2025

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Published on: July 14, 2023

Area of Science:

Endocrinology
Medical Informatics
Machine Learning

Background:

Multigland disease (MGD) affects 20-25% of primary hyperparathyroidism patients.
Preoperative imaging for MGD can be inaccurate or unnecessary.
Accurate MGD identification is crucial for guiding surgical approach and workup.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for predicting MGD in primary hyperparathyroidism.
To assess the utility of ML in distinguishing MGD from single adenoma.

Main Methods:

Retrospective analysis of a prospective database of 2010 patients.
Utilized the Waikato Environment for Knowledge Analysis (WEKA) ML platform.
Selected and compared models for overall accuracy (RandomTree) and MGD prediction (JRip with cost-sensitive learning).

Main Results:

The RandomTree classifier achieved 94.1% accuracy for overall prediction.
A cost-sensitive JRip classifier achieved 100% positive predictive value for MGD.
Preoperative imaging in the predicted MGD cohort showed low accuracy (29% for sestamibi, 36% for ultrasound).

Conclusions:

Machine learning models can effectively predict MGD in primary hyperparathyroidism.
ML aids in early clinical evaluation, guiding further diagnostic workup.
Predictive ML models can inform surgical planning and potentially reduce reliance on suboptimal imaging.