Bridging Radiology and AI: A Systematic Review of Deep Learning Models for Meniscus Tear Diagnosis Using Magnetic Resonance Imaging

Abstract

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system’s transformation is obtaining knowledge and meaningful information from complicated, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. Even after many current techniques were used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.