A machine learning based frame work for classification of neuromuscular disorders

Murthy, G. L. N.; Saii, G. Phawahan; Pavani, T.; Mohan, J. Lalith

doi:10.1109/icdds56399.2022.10037513

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Various features have been utilized as input for different ML algorithms, resulting in variable performance outcomes, 42,43 as can be seen in Table S1. 34,35,39,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59] The classification performance varied when distinguishing between normal and myopathic, normal and neuropathic, or myopathic and neuropathic conditions. For instance, TD and FD techniques with K-nearest neighbors as a classifier, can achieve 100% accuracy in a small dataset differentiating ALS from normal, but only 66% accuracy in differentiating myopathy from normal.…”

Section: Emg Signal Classificationmentioning

confidence: 99%

“…For instance, TD and FD techniques with K-nearest neighbors as a classifier, can achieve 100% accuracy in a small dataset differentiating ALS from normal, but only 66% accuracy in differentiating myopathy from normal. 44 A myriad of ML, [45][46][47][48][49] and DL 50,51,[57][58][59] techniques have also been used with variable performance accuracy.…”

Section: Emg Signal Classificationmentioning

confidence: 99%

“…Various features have been utilized as input for different ML algorithms, resulting in variable performance outcomes, 42,43 as can be seen in Table S1 34,35,39,42–59 . The classification performance varied when distinguishing between normal and myopathic, normal and neuropathic, or myopathic and neuropathic conditions.…”

Section: Ai In Edx Medicinementioning

confidence: 99%

See 2 more Smart Citations

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

Taha,

Morren

2023

Muscle and Nerve

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The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

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