Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning

Abstract

Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.

Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors

“…The optoelectronic motion-capture systems are much more expensive than depth sensors and they need to be installed in quite large rooms, which makes their application for in-home monitoring impractical. Platforms and treadmills equipped with force sensors, relatively common in clinical facilities and laboratories, are also more expensive and less convenient for in-home use than depth sensors, and they cannot serve for estimating the angles in the ankle, knee and hip joints—which is reportedly feasible using depth sensors [ 55 ]. Gait analysis systems based on wearable sensors are proliferating [ 17 ], but the need to wear a device on the body or clothes may be considered cumbersome by the potential users of such systems [ 56 ].…”

Application of Feedforward and Recurrent Neural Networks for Fusion of Data from Radar and Depth Sensors Applied for Healthcare-Oriented Characterisation of Persons’ Gait

“…Elderly persons are often reluctant to use the existing monitoring techniques because they may infringe on the privacy of those persons, or require the constant wearing of some additional devices [ 9 , 10 ]. As a result, two relatively new non-invasive and non-intrusive monitoring techniques are attracting growing attention of the researchers, viz., techniques based on depths sensors [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] and radar sensors [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series

“…The objective of this study is to quantitatively characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding of the loss of function and with potential for supporting diagnosis and progression evaluation. To the best of our knowledge this analysis has not yet been reported with patients suffering of V30M ATTRv amyloidosis, with only one study reporting a selection of spatiotemporal and angular parameters obtained with a RGB-D camera [ 20 ] and another using a machine learning model to distinguish between healthy and V30M ATTRv amyloidosis mutation carriers (with or without symptoms), also using gait information recorded with a RGB-D system [ 21 ].…”