RESUMEN

BACKGROUND: Knee ligament rupture is one of the most common injuries, but the diagnosis of its severity tends to require the use of complex methods and analyses that are not always available to patients. AIM: The objective of this research is the investigation and development of a diagnostic aid system to analyze and determine patterns that characterize the presence of the injury and its degree of severity. METHODS: Implement a novel proposal of a framework based on stacked auto-encoder (SAE) for ground reaction force (GRF) signals analysis, coming from the GaitRec database. Analysis of the raw data is used to determine the main features that allow us to diagnose the presence of a knee ligament rupture and classify its severity as high, mid or mild. RESULTS: The process is divided into two stages to determine the presence of the lesion and, if necessary, evaluate variations in features to classify the degree of severity as high, mid, and mild. The framework presents an accuracy of 87 % and a F1-Score of 90 % for detecting ligament rupture and an accuracy of 86.5 % and a F1-Score of 87 % for classifying severity. CONCLUSION: This new methodology aims to demonstrate the potential of SAE in physiotherapy applications as an evaluation and diagnostic tool, identifying irregularities associated with ligament rupture and its degree of severity, thus providing updated information to the specialist during the rehabilitation process.

Asunto(s)

Traumatismos de la Rodilla , Humanos , Rotura , Traumatismos de la Rodilla/diagnóstico por imagen , Traumatismos de la Rodilla/clasificación , Masculino , Femenino , Adulto , Procesamiento de Señales Asistido por Computador

RESUMEN

Systemic lupus erythematosus (SLE) is a rare, autoimmune disorder known to affect most organ sites. Complicating clinical management is a poorly differentiated, heterogenous SLE disease state. While some small molecule drugs and biologics are available for treatment, additional therapeutic options are needed. Parsing complex biological signatures using powerful, yet human interpretable approaches is critical to advancing our understanding of SLE etiology and identifying therapeutic repositioning opportunities. To approach this goal, we developed a semi-supervised deep neural network pipeline for gene expression profiling of SLE patients and subsequent characterization of individual gene features. Our pipeline performed exemplar multinomial classification of SLE patients in independent balanced validation (F1=0.956) and unbalanced, under-powered testing (F1=0.944) cohorts. A stacked autoencoder disambiguated individual feature representativeness by regenerating an input-like(A ') feature matrix. A to A' comparisons suggest the top associated features to be key features in gene expression profiling using neural nets.