A neural network-based software to recognise blepharospasm symptoms and to measure eye closure time

Computer Vision Technologies in Movement Disorders: A Systematic Review

BACKGROUND

Evaluation of movement disorders primarily relies on phenomenology. Despite refinements in diagnostic criteria, the accuracy remains suboptimal. Such a gap may be bridged by machine learning and video technology, which permit objective, quantitative, non-invasive motor analysis. Markerless automated video-analysis, namely Computer Vision, emerged as best suited for ecologically-valid assessment.

OBJECTIVES

To systematically review the application of Computer Vision for assessment, diagnosis, and monitoring of movement disorders.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched Cochrane, Embase, PubMed, and Scopus databases for articles published between 1984 and September 2024. We used the following search strategy: ("video analysis" OR "computer vision") AND ("Parkinson's disease" OR "PD" OR "tremor" OR "dystonia" OR "parkinsonism" OR "progressive supranuclear palsy" OR "PSP" OR "multiple system atrophy" OR "MSA" OR "corticobasal syndrome" OR "CBS" OR "chorea" OR "ballism" OR "myoclonus" OR "Tourette's syndrome").

RESULTS

Out of 1099 identified studies, 61 met inclusion criteria, and 10 additional studies were included based on authors' judgment. Parkinson's disease was the most investigated movement disorder, with gait as the prevalent motor task. OpenPose was the most used pose estimation software. Automated video-analysis consistently achieved diagnostic accuracies exceeding 80% across most movement disorders. For tremor, dystonia severity and tic detection, Computer Vision strongly aligned with accelerometery and clinical assessments.

CONCLUSIONS

Computer Vision holds potential to provide non-invasive quantification of presence and severity of movement disorders. Heterogeneity in video settings, software usage, and definition of standardized guidelines for videorecording are challenges to be addressed for real-word applications.

An Exploratory, Randomized, Double-Blind Clinical Trial of Dipraglurant for Blepharospasm

BACKGROUND

Blepharospasm is treated with botulinum toxin, but obtaining satisfactory results is sometimes challenging.

OBJECTIVE

The aim is to conduct an exploratory trial of oral dipraglurant for blepharospasm.

METHODS

This study was an exploratory, phase 2a, randomized, double-blind, placebo-controlled trial of 15 participants who were assigned to receive a placebo or dipraglurant (50 or 100 mg) and assessed over 2 days, 1 and 2 hours following dosing. Outcome measures included multiple scales rated by clinicians or participants, digital video, and a wearable sensor.

RESULTS

Dipraglurant was well tolerated, with no obvious impact on any of the measurement outcomes. Power analyses suggested fewer subjects would be required for studies using a within-subject versus independent group design, especially for certain measures. Some outcome measures appeared more suitable than others.

CONCLUSION

Although dipraglurant appeared well tolerated, it did not produce a trend for clinical benefit. The results provide valuable information for planning further trials in blepharospasm. © 2024 International Parkinson and Movement Disorder Society.

Quantitative evaluation of blinking in blepharospasm using electrooculogram-integrated smart eyeglasses

Smart eyeglasses with an integrated electrooculogram (EOG) device (JINS MEME ES_R^®, JINS Inc.) were evaluated as a quantitative diagnostic tool for blepharospasm. Participants without blepharospasm (n = 21) and patients with blepharospasm (n = 19) undertook two voluntary blinking tests (light and fast) while wearing the smart eyeglasses. Vertical (Vv) and horizontal (Vh) components were extracted from time-series voltage waveforms recorded during 30 s of the blinking tests. Two parameters, the ratio between the maximum and minimum values in the power spectrum (peak-bottom ratio, Fourier transform analysis) and the mean amplitude of the EOG waveform (peak amplitude analysis) were calculated. The mean amplitude of Vh from light and fast blinking was significantly higher in the blepharospasm group than in the control group (P  < 0.05 and P  < 0.05). Similarly, the peak-bottom ratio of Vv from light and fast blinking was significantly lower in the blepharospasm group than in the control group (P  < 0.05 and P  < 0.05). The mean amplitude of Vh and peak-bottom ratio of Vv correlated with the scores determined using the Jankovic rating scale (P  < 0.05 and P  < 0.01). Therefore, these parameters are sufficiently accurate for objective blepharospasm classification and diagnosis.

Digital Transformation in Ophthalmic Clinical Care During the COVID-19 Pandemic

ABSTRACT

COVID-19 has placed unprecedented pressure on health systems globally, whereas simultaneously stimulating unprecedented levels of transformation. Here, we review digital adoption that has taken place during the pandemic to drive improvements in ophthalmic clinical care, with a specific focus on out-of-hospital triage and services, clinical assessment, patient management, and use of electronic health records. We show that although there have been some successes, shortcomings in technology infrastructure prepandemic became only more apparent and consequential as COVID-19 progressed. Through our review, we emphasize the need for clinicians to better grasp and harness key technology trends such as telecommunications and artificial intelligence, so that they can effectively and safely shape clinical practice using these tools going forward.

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

Intelligent Neonatal Sepsis Early Diagnosis System for Very Low Birth Weight Infants

Neonatal sepsis is a critical pathology that particularly affects the neonates in intensive care, especially if they are preterm and low birth weight, with an incidence varying between 1and 40% according to the onset (early or late) of the disease. Prompt diagnostic and therapeutic interventions could reduce the high percentage of mortality that characterises this pathology, especially in the premature and low weight neonates. The HeRO score analyses the heart rate variability and represents the risk of contracting sepsis because of the hospitalization in the neonatal intensive care unit up to 24 h before the clinical signs. However, it has been demonstrated that the HeRO score can produce many false-positive cases, thus leading to the start of unnecessary antibiotic therapy. In this work, the authors propose an optimised artificial neural network model able to diagnose sepsis early based on the HeRO score along with a series of parameters strictly connected to the risk of neonatal sepsis. The proposed methodology shows promising results, outperforming the diagnostic accuracy of the only HeRO score and reducing the number of false positives, thus revealing itself to be a promising tool for supporting the clinicians in the daily clinical practice.

Extraction and selection of gait recognition features using skeleton point detection and improved fuzzy decision

It is of great importance to effectively measure gait features and recognize the signature gait patterns for gait rehabilitation. In this work, we used a skeleton point detection to extract gait features and proposed an improved fuzzy decision to select the most significant features for classifying gait patterns. Thirteen gait recognition features were extracted from the obtained skeleton points data. Taking the extracted features as an input, our improved fuzzy similarity priority decision method has obtained important sequences of all features based on the relatively important scores. Then, the ranked features were delivered in different classifiers by a sequential forward selection strategy to select the optimal feature subset. There were significant differences between groups in each of the thirteen gait recognition features (p < 0.005), indicating that all extracted features are potential influence factors for classifying gait patterns. We also found that the highest classification accuracy of 100% for gait feature subsets included the stride frequency, maximum flexion angle of knee, and toe-out angle, on the all classifiers. The results suggest that the proposed approaches are very useful in searching for the optimal feature subset in present dataset.