Combining Fiber Bragg Grating and Artificial Intelligence Technologies for Supporting Epidural Procedures.

OBJECTIVE: Loss of resistance (LOR) is a widely accepted method for performing epidural punctures in clinical settings. However, the risk of failure associated with LOR is still high. Solutions based either on Fiber Bragg grating sensors (FBG) or on artificial intelligence (AI) are gaining ground for supporting clinicians during this kind of procedure. Here, for the first time, we combined the mentioned two technologies to perform an AI-driven LOR identification based on data collected by a custom FBG sensor. METHODS: This study presented two contributions (i.e., automatic labeling and identification) based on machine learning to support epidural procedures by enhancing LOR detection. The methods were tested using data collected by a customized FBG-based flexible cap on 10 patients affected by chronic back pain. RESULTS: The automatic labeling can retrospectively identify every LOR event for each subject under consideration. This serves as the labeling for the automatic identification task, which emulates the real-time application of LOR detection. A Support Vector Machine, trained using a Leave-One-Out strategy, demonstrates high accuracy in identifying all LOR events while maintaining a minimal rate of false positives. CONCLUSION: Our findings revealed the promising performance of the proposed AI-based approach for automatic LOR detection. Thus, their combination with FBG technology can potentially improve the level of support offered to clinicians in this application. SIGNIFICANCE: The integration of AI and FBG technologies holds the promise of revolutionizing LOR detection, reducing the likelihood of unsuccessful epidural punctures and advancing pain management.

Machine learning for exploring neurophysiological functionality in multiple sclerosis based on trigeminal and hand blink reflexes.

Brainstem dysfunctions are very common in Multiple Sclerosis (MS) and are a critical predictive factor for future disability. Brainstem functionality can be explored with blink reflexes, subcortical responses consisting in a blink following a peripheral stimulation. Some reflexes are already employed in clinical practice, such as Trigeminal Blink Reflex (TBR). Here we propose for the first time in MS the exploration of Hand Blink Reflex (HBR), which size is modulated by the proximity of the stimulated hand to the face, reflecting the extension of the peripersonal space. The aim of this work is to test whether Machine Learning (ML) techniques could be used in combination with neurophysiological measurements such as TBR and HBR to improve their clinical information and potentially favour the early detection of brainstem dysfunctionality. HBR and TBR were recorded from a group of People with MS (PwMS) with Relapsing-Remitting form and from a healthy control group. Two AdaBoost classifiers were trained with TBR and HBR features each, for a binary classification task between PwMS and Controls. Both classifiers were able to identify PwMS with an accuracy comparable and even higher than clinicians. Our results indicate that ML techniques could represent a tool for clinicians for investigating brainstem functionality in MS. Also, HBR could be promising when applied in clinical practice, providing additional information about the integrity of brainstem circuits potentially favouring early diagnosis.

Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application.

BACKGROUND: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were developed to forecast future glycemic levels and to allow patients to avoid adverse events. Most models are tuned on data of adult patients, whereas the prediction of glycemic levels of pediatric patients has been rarely investigated, as they represent the most challenging T1D population. METHODS: A Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) Recurrent Neural Network were optimized on glucose, insulin, and meal data of 10 virtual pediatric patients. The trained models were then implemented on two edge-computing boards to evaluate the feasibility of an edge system for glucose forecasting in terms of prediction accuracy and inference time. RESULTS: The LSTM model achieved the best numeric and clinical accuracy when tested in the .tflite format, whereas the CNN achieved the best clinical accuracy in uint8. The inference time for each prediction was far under the limit represented by the sampling period. CONCLUSION: Both models effectively predict glucose in pediatric patients in terms of numerical and clinical accuracy. The edge implementation did not show a significant performance decrease, and the inference time was largely adequate for a real-time application.

Natural language processing in low back pain and spine diseases: A systematic review.

Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models' points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders.

Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.

Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Machine Learning", "Deep Learning", "Neural Network", "Computer Aided Diagnosis", "Low Back Pain", "Lumbar", "Intervertebral Disc Degeneration", "Spine Surgery", etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.

Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques.

BACKGROUND: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. METHODS: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). RESULTS: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. CONCLUSIONS: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

Positive tissue transglutaminase antibodies with negative endomysial antibodies: Unresolved issues in diagnosing celiac disease.

BACKGROUND: The serological screening for celiac disease (CD) is currently based on the detection of anti-transglutaminase (tTG) IgA antibodies, subsequently confirmed by positive endomysial antibodies (EMA). When an anti-tTG IgA positive/EMA IgA negative result occurs, it can be due either to the lower sensitivity of the EMA test or to the lower specificity of the anti-tTG test. This study aimed at verifying how variation in analytical specificity among different anti-tTG methods could account for this discrepancy. METHODS: A total of 130 consecutive anti-tTG IgA positive/EMA negative samples were collected from the local screening routine and tested using five anti-tTG IgA commercial assays: two chemiluminescence methods, one fluoroimmunoenzymatic method, one immunoenzymatic method and one multiplex flow immunoassay method. RESULTS: Twenty three/130 (17.7%) patients were diagnosed with CD. In the other 107 cases a diagnosis of CD was not confirmed. The overall agreement among the five anti-tTG methods ranged from 28.5% to 77.7%. CD condition was more likely linked to the positivity of more than one anti-tTG IgA assay (monopositive = 2.5%, positive with ≥ three methods = 29.5%; p = 0.0004), but it was not related to anti-tTG IgA antibody levels (either positive or borderline; p = 0.5). CONCLUSIONS: Patients with positive anti-tTG/negative EMA have a low probability of being affected by CD. Given the high variability among methods to measure anti-tTG IgA antibodies, anti-tTG-positive/EMA-negative result must be considered with extreme caution. It is advisable that the laboratory report comments on any discordant results, suggesting to consider the data in the proper clinical context and to refer the patient to a CD reference center for prolonged follow up.