Background: Over one billion people worldwide suffer from neurological conditions that cause mobility impairments, often persisting despite rehabilitation. Chronic neurological disease (CND) patients who lack access to continuous rehabilitation face gradual functional decline. The International Classification of Functioning, Disability, and Health (ICF) provides a comprehensive framework for assessing these patients. Objective: This study aims to evaluate the outcomes of a non-hospitalized neuromotor rehabilitation project for CND patients in Italy using the Barthel Index (BI) as the primary outcome measure. The rehabilitation was administered through an Individual Rehabilitation Plan (IRP), tailored by a multidisciplinary team and coordinated by a physiatrist. The IRP involved an initial comprehensive assessment, individualized therapy administered five days a week, and continuous adjustments based on patient progress. The secondary objectives include assessing mental status and sensory and communication functions, and identifying predictive factors for BI improvement using an artificial neural network (ANN). Methods: A retrospective observational study of 128 CND patients undergoing a rehabilitation program between 2018 and 2023 was conducted. Variables included demographic data, clinical assessments (BI, SPMSQ, and SVaMAsc), and ICF codes. Data were analyzed using descriptive statistics, linear regressions, and ANN to identify predictors of BI improvement. Results: Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, p < 0.001). Patients with severe mobility issues showed the most difficulty in transfers and walking, as indicated by the ICF E codes. Females, especially older women, experienced more cognitive decline, affecting rehabilitation outcomes. ANN achieved 86.4% accuracy in predicting BI improvement, with key factors including ICF mobility codes and the number of past rehabilitation projects. Conclusions: The ICF mobility codes are strong predictors of BI improvement in CND patients. More rehabilitation sessions and targeted support, especially for elderly women and patients with lower initial BI scores, can enhance outcomes and reduce complications. Continuous rehabilitation is essential for maintaining progress in CND patients.

Advance assessment of the potential functional improvement of patients undergoing a rehabilitation program is crucial in developing precision medicine tools and patient-oriented rehabilitation programs, as well as in better allocating resources in hospitals. In this work, we propose a novel approach to this problem using machine learning algorithms focused on assessing the modified Barthel index (mBI) as an indicator of functional ability. We build four tree-based ensemble machine learning models and train them on a private training cohort of orthopedic (OP) and neurological (NP) hospital discharges. Moreover, we evaluate the models using a validation set for each category of patients using root mean squared error (RMSE) as an absolute error indicator between the predicted mBI and the actual values. The best results obtained from the study are an RMSE of 6.58 for OP patients and 8.66 for NP patients, which shows the potential of artificial intelligence in predicting the functional improvement of patients undergoing rehabilitation.

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model’s effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively.