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

Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis

Neurological diseases are increasingly diverse and prevalent, presenting significant challenges for their timely and accurate diagnosis. The aim of the present study is to conduct a bibliometric analysis and literature review in the field of neurology to explore advancements in the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL). Using VOSviewer software (version 1.6.20.0) and documents retrieved from the Scopus database, the analysis included 113 articles published between 1 January 2018 and 31 December 2024. Key journals, authors, and research collaborations were identified, highlighting major contributions to the field. Science mapping investigated areas of research focus, such as biomechanical data and gait analysis including AI methodologies for neurological disease diagnosis. Co-occurrence analysis of author keywords allowed for the identification of four major themes: (a) machine learning and gait analysis; (b) sensors and wearable health technologies; (c) cognitive disorders; and (d) neurological disorders and motion recognition technologies. The bibliometric insights demonstrate a growing but relatively limited collaborative interest in this domain, with only a few highly cited authors, documents, and journals driving the research. Meanwhile, the literature review highlights the current methodologies and advancements in this field. This study offers a foundation for future research and provides researchers, clinicians, and occupational therapists with an in-depth understanding of AI's potentially transformative role in neurology.

Gait abnormalities in older adults with transthyretin cardiac amyloidosis

BACKGROUND

Transthyretin cardiac amyloidosis (ATTR cardiac amyloidosis) is caused by variant (ATTRv) or wild type (ATTRwt) transthyretin. While gait abnormalities have been studied in younger patients with ATTRv amyloidosis, research on gait in older adults with ATTR cardiac amyloidosis is lacking. Given ATTR cardiac amyloidosis' association with neuropathy and orthopedic manifestations, we explore the gait in this population.

METHODS

Twenty-eight older male ATTR cardiac amyloidosis patients and 11 healthy older male controls walked overground with and without a dual cognitive task. Gait parameters: stride width, length, velocity and stance time percentage were measured using an instrumented mat. ATTR amyloidosis patients were further categorized based on clinical and functional assessments.

RESULTS

We found significant gait differences between ATTR cardiac amyloidosis patients and healthy controls; patients had more variable, slower, narrower and shorter strides, with their feet spending more time in contact with the ground as opposed to in swing. However, the observed gait differences did not correlate with clinical and functional measures of ATTR cardiac amyloidosis severity.

CONCLUSIONS

Our results suggest that gait analysis could be a complementary tool for characterizing ATTR cardiac amyloidosis patients and may inform clinical care as it relates to falls, management of anticoagulation, and functional independence.

Optimal practices for the management of hereditary transthyretin amyloidosis: real-world experience from Japan, Brazil, and Portugal

Hereditary transthyretin (ATTRv) amyloidosis is a rare and autosomal dominant disorder associated with mutations in the transthyretin gene. Patients present with diverse symptoms related to sensory, motor, and autonomic neuropathy, as well as gastrointestinal, ocular, cardiac, renal and orthopedic symptoms, resulting from the deposition of transthyretin amyloid fibrils in multiple organs. The progressive nature of ATTRv amyloidosis necessitates pre- and post-onset monitoring of the disease. This review article is primarily based on a collation of discussions from a medical advisory board meeting in August 2021. In this article, we summarize the best practices in amyloidosis centers in three major endemic countries for ATTRv amyloidosis (Japan, Brazil, and Portugal), where most patients carry the Val30Met mutation in the transthyretin gene and the patients' genetic background was proven to be the same. The discussions highlighted the similarities and differences in the management of asymptomatic gene mutation carriers among the three countries in terms of the use of noninvasive tests and tissue biopsies and timing of starting the investigations. In addition, this article discusses a set of practical tests and examinations for monitoring disease progression applicable to neurologists working in diverse medical settings and generalizable in non-endemic countries and areas. This set of assessments consists of periodic (every 6 to 12 months) evaluations of patients' nutritional status and autonomic, renal, cardiac, ophthalmologic, and neurological functions. Physical examinations and patient-reported outcome assessments should be also scheduled every 6 to 12 months. Programs for monitoring gene mutation carriers and robust referral networks can aid in appropriate patient management in pre- to post-onset stages. For pre- and post-symptom onset testing for ATTRv amyloidosis, various noninvasive techniques are available; however, their applicability differs depending on the medical setting in each country and region, and the optimal option should be selected in view of the clinical settings, medical environment, and available healthcare resources in each region.

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

In this paper, the useability of feedforward and recurrent neural networks for fusion of data from impulse-radar sensors and depth sensors, in the context of healthcare-oriented monitoring of elderly persons, is investigated. Two methods of data fusion are considered, viz., one based on a multilayer perceptron and one based on a nonlinear autoregressive network with exogenous inputs. These two methods are compared with a reference method with respect to their capacity for decreasing the uncertainty of estimation of a monitored person's position and uncertainty of estimation of several parameters enabling medical personnel to make useful inferences on the health condition of that person, viz., the number of turns made during walking, the travelled distance, and the mean walking speed. Both artificial neural networks were trained on the synthetic data. The numerical experiments show the superiority of the method based on a nonlinear autoregressive network with exogenous inputs. This may be explained by the fact that for this type of network, the prediction of the person's position at each time instant is based on the position of that person at the previous time instants.

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

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people's risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors' viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject's clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.

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

Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.