Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data

Clinical Application of an Artificial Intelligence System for Diagnosing Thyroid Disease Based on a Computer Neural Network Deep Learning Model

Purpose

This study aimed to establish a stereoscopic neural learning network through deep learning and construct an artificial intelligence (AI) diagnosis system for the prediction of benign and malignant thyroid diseases, as well as repeatedly verified the diagnosis system and adjusted the data, in order to develop a type of AI-assisted thyroid diagnosis software with a low false negative rate and high sensitivity for clinical practice.

Patients and Methods

From July 2020 to April 2023, A total of 36 patients with thyroid nodules in our hospital were selected for diagnosis of thyroid nodules based on the Expert Consensus on Thyroid Ultrasound; samples were taken by aspiration biopsy or surgically and sent for pathological diagnosis. The ultrasonic diagnosis results were compared with the pathological results, a database was established based on the ultrasonic diagnostic characteristics and was entered in the AI-assisted diagnosis software for judgment of benign and malignant conditions. The data in the software were corrected based on the conformity rate and the reasons for misjudgment, and the corrected software was used to evaluate the benign and malignant conditions of the 36 patients, until the conformity rate exceeded 90%.

Results

The initial conformity rate of the AI software for identifying benign and malignant conditions was 88%, while that of the software utilizing the database was 94%.

Conclusion

We established a stereoscopic neural learning network and construct an AI diagnosis system for the prediction of benign and malignant thyroid diseases, with a low false negative rate and high sensitivity for clinical practice.

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.