10 research outputs found
Exploring ChatGPT’s potential in the clinical stream of neurorehabilitation
In several medical fields, generative AI tools such as ChatGPT have achieved optimal performance in identifying correct diagnoses only by evaluating narrative clinical descriptions of cases. The most active fields of application include oncology and COVID-19-related symptoms, with preliminary relevant results also in psychiatric and neurological domains. This scoping review aims to introduce the arrival of ChatGPT applications in neurorehabilitation practice, where such AI-driven solutions have the potential to revolutionize patient care and assistance. First, a comprehensive overview of ChatGPT, including its design, and potential applications in medicine is provided. Second, the remarkable natural language processing skills and limitations of these models are examined with a focus on their use in neurorehabilitation. In this context, we present two case scenarios to evaluate ChatGPT ability to resolve higher-order clinical reasoning. Overall, we provide support to the first evidence that generative AI can meaningfully integrate as a facilitator into neurorehabilitation practice, aiding physicians in defining increasingly efficacious diagnostic and personalized prognostic plans
Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way?
One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. This study aims at providing this comparison on a sample of TBI patients evaluated at baseline (T0), after 3 months from the event (T1), and at discharge (T2). A Classical Linear Regression Model (LM) was compared with independent performances of Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes (NB) and Decision Tree (DT) algorithms, together with an ensemble ML approach. The accuracy was similar among LM and ML algorithms on the analyzed sample when two classes of outcome (Positive vs. Negative) approach was used, whereas the NB algorithm showed the worst performance. This study highlights the utility of comparing traditional regression modeling to ML, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons
TeleRehabilitation of Social-Pragmatic Skills in Children with Autism Spectrum Disorder: A Principal Component Analysis
In many therapeutic settings, remote health services are becoming increasingly a viable strategy for behavior management interventions in children with autism spectrum disorder (ASD). However, there is a paucity of tools for recovering social-pragmatic skills. In this study, we sought to demonstrate the effectiveness of a new online behavioral training, comparing the performance of an ASD group carrying out an online treatment (n°8) with respect to a control group of demographically-/clinically matched ASD children (n°8) engaged in a traditional in-presence intervention (face-to-face). After a 4-month behavioral treatment, the pragmatic skills language (APL test) abilities detected in the experimental group were almost similar to the control group. However, principal component analysis (PCA) demonstrated that the overall improvement in socio-pragmatic skills was higher for ASD children who underwent in-presence training. In fact, dimensions defined by merging APL subscale scores are clearly separated in ASD children who underwent in-presence training with respect to those performing the online approach. Our findings support the effectiveness of remote healthcare systems in managing the social skills of children with ASD, but more approaches and resources are required to enhance remote services
Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics
Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics
Multiparametric Evaluation of Geriatric Patients Admitted to Intermediate Care: Impact on Geriatric Rehabilitation
Optimizing the functional status of patients of any age is a major global public health goal. Rehabilitation is a process in which a person with disabilities is accompanied to achieve the best possible physical, functional, social, intellectual, and relational outcomes. The Intermediate Care Unit within the O.U. of Geriatrics and Gerontology of the San Martino Hospital in Genoa is focused on the treatment and motor reactivation of patients with geriatric pathologies. The objective of this study was to identify which factor, among the characteristics related to the patient and those identified by the geriatric evaluation, had the greatest impact on rehabilitation outcomes. Our findings revealed significant correlations between the Barthel Index delta, the 4AT Screening Test, and the number of drugs taken. This association highlights the potential benefits of medication management in enhancing the overall well-being and functional abilities of frail older adults, despite the literature suggesting that polypharmacotherapy is associated with a reduction in functional status and an increase in mortality. These findings underscore the significance of a multidimensional geriatric assessment. Refining and optimising these multidisciplinary approaches is the objective of a more effective geriatric rehabilitation strategy
Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach
Aim: The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. Materials and Methods: We applied state-of-the-art machine learning algorithms to evaluate the best predictors of the outcome, and 100 RC patients were evaluated at baseline (T0), after 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) from surgical intervention. The outcome measure was the Costant–Murley Shoulder Score, whereas age, sex, BMI, the 36-Item Short-Form Survey, the Simple Shoulder Test, the Hospital Anxiety and Depression Scale, the American Shoulder and Elbow Surgeons Score, the Oxford Shoulder Score, and the Shoulder Pain and Disability Index were considered as predictive factors. Support vector machine (SVM), k-nearest neighbors (k-NN), naïve Bayes (NB), and random forest (RF) algorithms were employed. Results: Across all sessions, the classifiers demonstrated suboptimal performance when using both the complete and shrunken sets of features. Specifically, the logistic regression (LR) classifier achieved a mean accuracy of 46.5% ± 6%, while the random forest (RF) classifier achieved 51.25% ± 4%. For the shrunken set of features, LR obtained a mean accuracy of 48.5% ± 6%, and RF achieved 45.5% ± 4.5%. No statistical differences were found when comparing the performance metrics of ML algorithms. Conclusions: This study underlines the importance of extending the application of AI methods to new predictors, such as neuroimaging and kinematic data, in order to better record significant shifts in RC patients’ prognosis. Limitations: The data quality within the cohort could represent a limitation, since certain variables, such as smoking, diabetes, and work injury, are known to have an impact on the outcome
Heterologous COVID-19 Booster Vaccination in the Chronic Disorder of Consciousness: A Pilot Study
Significant anti-spike protein receptor-binding domain (S-RBD) antibody responses have been demonstrated in patients with chronic disorder of consciousness (DOC) completing a COVID-19 vaccine regime with BNT162b2 (Pfizer–BioNTech). We now provide further prospective data on the immunogenicity of these patients followed by heterologous booster injection with mRNA-1273 (Moderna). These patients were compared with two different demographically comparable healthcare workers (HCW) groups who underwent homologous booster injection with BNT162b2 vaccine or heterologous booster injection with mRNA-1273. Antibody responses were evaluated at 21 days after the administration of the booster dose of vaccination. Results: No severe adverse reactions were reported after each type of vaccination. Heterologous boosting with mRNA-1273 elicited a higher increase of S-RBD IgG levels than homologous boosting with BNT162b2 both in DOC patients and HCW who had previously received two doses of BNT162b2. No significant difference was detected between DOC and HCW patients who received heterologous boosting. Conclusions: Despite the small sample size, our preliminary results suggest that heterologous boosting with mRNA-1273, following initial vaccination with BNT162b2, is safe and tends to be more immunogenic than homologous boosting, either in fragile people or in healthy controls
A deep learning approach for automatic video coding of deictic gestures in children with autism
Autism is a heterogeneous neurodevelopmental condition characterized by impairments in social communication, along with restrictive and repetitive patterns of interests and behaviors and sensory atypicalities. Early impairments in gestural communication, especially in deictic gestures, are significantly associated with autism and strong predictors of language development. Despite the implication of deictic gestures in autism has been acknowledged, it has not been sufficiently explored by artificial intelligence. To address this, the paper proposes an automatic digital coding approach based on deep learning models. By using a transformer architecture, a multi-frame modeling strategy has been implemented and applied on 37 video clips of naturalistic mother-child interactions with the aim to recognize four main deictic gestures: pointing, giving, showing and requesting. The system was trained and validated on 31 clips, internally tested on 6 clips and externally tested on 5 extra clips, using Python. Preprocessing phase involves using a 1024 feature extractor based on Densenet pretrained on Imagenet. Preliminary results showed respectively 100% of accuracy for training set, 80% for validation set and 67% for internal testing set. These findings suggest that the proposed system is a very promising approach for the automatic analysis of deictic gestures. In future work, we plan to validate our model on a larger number of samples to achieve higher and more reliable performances