Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism

Imaging Recommendations for Diagnosis and Management of Primary Parathyroid Pathologies: A Comprehensive Review

Parathyroid pathologies are suspected based on the biochemical alterations and clinical manifestations, and the predominant roles of imaging in primary hyperparathyroidism are localisation of tumour within parathyroid glands, surgical planning, and to look for any ectopic parathyroid tissue in the setting of recurrent disease. This article provides a comprehensive review of embryology and anatomical variations of parathyroid glands and their clinical relevance, surgical anatomy of parathyroid glands, differentiation between multiglandular parathyroid disease, solitary adenoma, atypical parathyroid tumour, and parathyroid carcinoma. The roles, advantages and limitations of ultrasound, four-dimensional computed tomography (4DCT), radiolabelled technetium-99 (99mTc) sestamibi or dual tracer 99mTc pertechnetate and 99mTc-sestamibi with or without single photon emission computed tomography (SPECT) or SPECT/CT, dynamic enhanced magnetic resonance imaging (4DMRI), and fluoro-choline positron emission tomography (18F-FCH PET) or [11C] Methionine (11C -MET) PET in the management of parathyroid lesions have been extensively discussed in this article. The role of fluorodeoxyglucose PET (FDG-PET) has also been elucidated in this article. Management guidelines for parathyroid carcinoma proposed by the American Society of Clinical Oncology (ASCO) have also been described. An algorithm for management of parathyroid lesions has been provided at the end to serve as a quick reference guide for radiologists, clinicians and surgeons.

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Comparing accuracy of machine learning approaches to identifying parathyroid adenomas: Lessons and new directions

PURPOSE

The purpose of this investigation is to understand the accuracy of machine learning techniques to detect biopsy-proven adenomas from similar appearing lymph nodes and factors that influence accuracy by comparing support vector machine (SVM) and bidirectional Long short-term memory (Bi-LSTM) analyses. This will provide greater insight into how these tools could integrate multidimensional data and aid the detection of parathyroid adenomas consistently and accurately.

METHODS

Ninety-nine patients were identified; 93 4D-CTs of patients with pathology-proven parathyroid adenomas were reviewed; 94 parathyroid adenomas and 112 lymph nodes were analyzed. A 2D slice through the lesions in each phase was used to perform sequence classification with ResNet50 as the pre-trained network to construct the Bi-LSTM model, and the mean enhancement curves were used to form an SVM model. The model characteristics and accuracy were calculated for the training and validation data sets.

RESULTS

On the training data, the area under the curve (AUC) of the Bi-LSTM was 0.99, while the SVM was 0.95 and statistically significant on the DeLong test. The overall accuracy of the Bi-LSTM on the validation data set was 92 %, while the SVM was 88 %. The accuracy for parathyroid adenomas specifically was 93 % for the Bi-LSTM and 83 % for the SVM model.

CONCLUSION

Enhancement characteristics are a distinguishing feature that accurately identifies parathyroid adenomas alone. The Bi-LSTM performs statistically better in identifying parathyroid adenomas than the SVM analysis when using both morphologic and enhancement information to distinguish between parathyroid adenomas and lymph nodes.

SUMMARY STATEMENT

The Bi-LSTM more accurately identifies parathyroid adenomas than the SVM analysis, which uses both morphologic and enhancement information to distinguish between parathyroid adenomas and lymph nodes, performs statistically better.

Future Directions in the Treatment of Thyroid and Parathyroid Disease

The surgical management of thyroid and parathyroid disease has evolved considerably since the era of Theodor Kocher. We review the current trends in thyroid and parathyroid surgery concerning robotic surgery for remote access, the use of parathyroid autofluorescence detection technology to aid in the prevention of hypocalcemia as well as the use of thermal ablation to target thyroid nodules in a minimally invasive way. We also discuss how artificial intelligence is being used to improve the workflow and diagnostics preoperatively as well as for intraoperative decision-making. We also discuss potential areas where future research may enhance outcomes.

Parathyroid Imaging

Primary hyperparathyroidism (1° HPT) is a relatively common endocrine disorder usually caused by autonomous secretion of parathormone by one or several parathyroid adenomas. 1° HPT causing hypercalcemia, kidney stones and/or osteoporosis should be treated whenever possible by parathyroidectomy. Accurate preoperative location of parathyroid adenomas is crucial for surgery planning, mostly when performing minimally invasive surgery. Cervical ultrasonography (US) is usually performed to localize parathyroid adenomas as a first intention, followed by 99mTc- sestamibi scintigraphy with SPECT/CT whenever possible. 4D-CT is a possible alternative to 99mTc- sestamibi scintigraphy. Recently, 18F-fluorocholine positron emission tomography/computed tomography (18F-FCH PET/CT) has made its way in the clinics as it is the most sensitive method for parathyroid adenoma detection. It can eventually be combined to 4D-CT to increase its diagnostic performance, although this results in higher dose exposure to the patient. Other forms of hyperparathyroidism consist in secondary (2° HPT) and tertiary hyperparathyroidism (3° HPT). As parathyroidectomy is not usually part of the management of patients with 2° HPT, parathyroid imaging is not routinely performed in these patients. In patients with 3° HPT, total or subtotal parathyroidectomy is often performed. Localization of hyperfunctional glands is an important aid to surgery planning. As 18F-FCH PET/CT is the most sensitive modality in multigland disease, it is the preferred imaging technic in 3° HPT patients, although its cost and availability may limit its widespread use in this setting.

A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy with 99mTc-Sestamibi

Background: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the localisation of parathyroid glands (PGs) and assist in surgery. Artificial intelligence and, more specifically, image detection methods, can assist medical experts and reduce the workload in their everyday routine. Methods: The present study employs an innovative CNN topology called ParaNet, to analyse early MIBI, late MIBI, and TcO4 thyroid scan images simultaneously to perform first-level discrimination between patients with abnormal PGs (aPG) and patients with normal PGs (nPG). The study includes 632 parathyroid scans. Results: ParaNet exhibits a top performance, reaching an accuracy of 96.56% in distinguishing between aPG and nPG scans. Its sensitivity and specificity are 96.38% and 97.02%, respectively. PPV and NPV values are 98.76% and 91.57%, respectively. Conclusions: The proposed network is the first to introduce the automatic discrimination of PG and nPG scans acquired by scintigraphy with ^99mTc-sestamibi (MIBI). This methodology could be applied to the everyday routine of medics for real-time evaluation or educational purposes.