Artificial intelligence (machine learning) as a screening tool for mri evaluation of normal and abnormal medial meniscus

Abstract

Objective: The most common cause of abnormal meniscus is due to sports-related injuries and the other causes are osteoarthritic and non-osteoarthritic menisci. Sports injuries of meniscus are most common in individuals engaged in high-impact or pivot-heavy activities. The meniscus, a C-shaped piece of cartilage within the knee, cushions and absorbs the applied loads and helps stabilize the knee. A torn/osteoarthritic meniscus can lead to knee pain, swelling, and impairment of function. MRI stands as the most reliable imaging technique to be used in the detection of abnormal meniscus since it offers excellent soft tissue resolution between cartilage, tendons, and ligaments. Although much advancement has been realized in the MRI technology, the diagnosis of meniscal pathologies from MRI images remains one of the difficult areas. The problem is that the signals coming frommeniscal tissue are difficult to distinguish from those of the ligaments andother fluid-filled structures surrounding them, therefore making differentiation of normal against injured/degenerated meniscus a very difficult task. Segmentation of the meniscus, where the meniscus is separated from other structures in the MRI images, forms an important step in improving diagnostic accuracy. Such detailed visualization of the meniscus allows clinicians to evaluate its shape, size, and volume; it can also quantify several specific parameters, such as thickness or degeneration. Manual segmentation is an extremely tedious and very time-consuming process that requires a lot of expertise and experience. Consequently, manual segmentation is prone to variability when different clinicians apply the technique, causing major inconsistencies in diagnostics. Recent advancements in deep learning and artificial intelligence made possible promising solutions in the automation of segmentation. Algorithm fully and semi-automated were developed and make a wide utilization of machine learning models like CNNs in the identification and segmentation of the meniscus in MRI images. Those models are trained on large, labeled datasets of MRI scans with meniscal tissue and surrounding structures, learning how to distinguish the meniscal tissue from the surrounding structure. The benefits of automated segmentation might involve elevated diagnostic precision, higher workflow efficiency, and lesser human error. while much promise is shown to be held by AI-based methods, the challenges are yet present in reality.

Results: This paper will detail the work to develop a deep learning model based on Mask R-CNN for abnormal meniscus detection and diagnosis from MRI images. The model aims at achieving in detecting normal menisci (healthy) and abnormal menisci (torn/degenerated menisci) with AUC of 0.992 for detecting normal menisci and AUC of 0.962 for detecting abnormal menisci. Overall, this methodology manifests much promise as an effective tool for radiologists to help diagnose injuries of the meniscus.

Conclusion: We introduce a new algorithm that utilizes mask-region convolutional neural networks (CNNs) to effectively identify normal and abnormal meniscus. This advancement lays the groundwork for creating a complete, automated solution for diagnosing this condition.