Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases [version 1; peer review: awaiting peer review]

• Published in: F1000 research 2023, Vol.12, p.855
• Main Authors: Surendran, Aathira, Shrivastav, Sunita, Srivastav, Gaurav
• Format: Article
• Language: English
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.133328.1

Introduction: Temporomandibular disorder (TMD) encompasses several clinical manifestations, which are characterized by temporomandibular joint and masticatory muscle discomfort and dysfunction (TMJ). The best imaging technique for evaluating TMJ is magnetic resonance imaging (MRI), which makes it possible to see the anatomical and pathological characteristics of every joint component. In recent years, convolutional neural networks -based deep learning algorithms have been favoured because of their outstanding capability in recognizing objects in medical images. The objective of this study is to assess, compare and co-relate articular disc morphology by automated segmentation using deep learning on MRI images in skeletal Class II (vertical growth pattern) TMD cases as compared to skeletal Class II (horizontal growth pattern) TMD cases and Class I non-TMD cases Methods: Grading of skeletal Class II (vertical growth pattern) cases and skeletal Class II (horizontal growth pattern) cases based on severity of TMD will be carried out using diagnostic criteria for temporomandibular disorders. Bilateral sagittal as well as coronal MRI images will be obtained. A convolutional neural network (CNN) encoder-decoder named U-Net will be used to segment the articular disc on MRI. Understanding the nature of variations between Class I and both types of Class IIs will help orthodontists to better predict the potential risk for the development of TMDs and accordingly take precautions while doing treatment in such cases. Moreover, it can be used to automate TMD diagnosis and other smart applications. Conclusions: This study will aid in identifying articular disc morphology on MRI. The deep learning algorithms with effective data augmentation may perform better in MRI readings than human clinicians when using the same data, which will be advantageous for TMD diagnosis.