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MedTech Outlook | Friday, August 23, 2019
The integration of artificial neural networks and machine learning has enabled the development of robust deep learning models in the biomechanics field.
FREMONT, CA – The last few years have witnessed significant revolutions in technology. Artificial intelligence (AI) has evolved from the field of traditional computer science, establishing its presence in various sectors and industries, including biomechanics. The capabilities of AI technology are steadily transforming healthcare at science, clinical, and management levels.
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AI has shown significant potential in the biomechanics landscape, leading to the development of intelligent diagnostic tools for assessing various mechanical conditions of the biological system. Artificial neural networks (ANNs) are leveraged to study movement optimization to forecast an optimal approach for biomechanics operations.
ANN enables computers to analyze and learn, facilitating a mathematical model of neurons in the brain. They represent non-linear systems such as human movements to form the notational analysis perspective. The data acquired by the ANNs are stored in multiple layers. However, the networks require vast training datasets before they can be sent for testing.
The integration of ANN technology with modern machine learning (ML) has led to the emergence of deep learning. The computational boost offered by the new GPUs has enabled the systems to process vast troves of datasets. It has enhanced the capabilities of computer vision, including image classification, object detection, face recognition, and optical character recognition (OCR), and natural language processing (NLP).
The deep learning models are developed by superimposing layers of neurons. The models are trained using backpropagation algorithms, empowering the machines to compute the representation in the various layers. Once activated, the deep learning models can automatically learn intricate patterns from high-dimensional raw data with minimal guidance.
By leveraging ML techniques, clinicians have been able to enhance patient care, using robust models to extract relevant data on treatment patterns. ML is being used by medical organizations to sort through the vast digital health data and come up with insights to predict the effect of drugs.
AI has made significant progress in musculoskeletal medicine, allowing greater understanding of biomechanics based on anatomical shape assessment. Analyzing the complex psychological data using ML has enabled gait analysis, enhancing the diagnostic accuracy in patients with spinal stenosis. The development of gait model by contrasting patient motion with normal controls using a support vector machine (SVM) has enabled better diagnosis.
The complex gait analysis data has led to the development of sophisticated models to estimate the presence of various conditions. The engineering approach to different medical conditions has enabled solution providers in the healthcare sector to develop robust models, including deformable joint contact models for estimating loading conditions in implant-implant, human-orthotic, and foot-ground interactions.
ANN-based contact models can compute data thousands of times faster than conventional models, thus eliminating downtime during musculoskeletal simulations. The approach is leveraged by surgeons across the world for the analysis of preoperative images. It has facilitated significant advancements in medical imaging pathway, including acquisition, reconstruction, analysis, and interpretation.
The conventional approaches utilized for sorting the images into homogeneous partitions during cartilage lesion assessments is a time-consuming task with limited or no standardization. However, the incorporation of ML technology has enabled clinicians to streamline the analysis of wrist, hip, and knee cartilage MRI. The technology can also be leveraged by unskilled users in remote locations to diagnose patients and provide care.
As imaging and health data increases, the use of ML will increase significantly. Also, the incorporation of computer-aided detection (CADe) systems into the diagnostic processes can substantially improve the analysis, thus bolstering the clinical decision-making process, thus leading to the reduction in reading time, increase in detection sensitivity, and augmentation of diagnostic accuracy.
The advancements in the AI sector are continuously enhancing the speed and accuracy of analysis models, offering great potential for the medical industry. Although the implementation of AI has garnered both enthusiasm and skepticism, its potential in automating the repetitive tasks and bringing higher productivity and efficiency is indisputable.
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