Machine learning in Neurology - A Technological Based Review



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Computer Vision

Neuroradiology
Digital Pathology
Other Camera Based

NeuroRadiology

Utility of Artificial Intelligence Tool as a Prospective Radiology Peer Reviewer β€” Detection of Unreported Intracranial Hemorrhage
Rationale and Objectives: Misdiagnosis of intracranial hemorrhage (ICH) can adversely impact patient outcomes. The increasing workload on the radiologists may increase the chance of error and compromise the quality of care provided by the radiologists.
Materials and Methods: We used an FDA approved artificial intelligence (AI) solution based on a convolutional neural network to assess the prevalence of ICH in scans, which were reported as negative for ICH. We retrospectively applied the AI solution to all consecutive noncontrast computed tomography (CT) head scans performed at eight imaging sites affiliated to our institution.
Results: In the 6565 noncontrast CT head scans, which met the inclusion criteria, 5585 scans were reported to have no ICH (β€œnegative-by-report” cases). We applied AI solution to these β€œnegative-by-report” cases. AI solution suggested there were ICH in 28 of these scans (β€œnegative-by-report” and β€œpositive-by-AI solution”). After consensus review by three neuroradiologists, 16 of these scans were found to have ICH, which was not reported (missed diagnosis by radiologists), with a false-negative rate of radiologists for ICH detection at 1.6%. Most commonly missed ICH was overlying the cerebral convexity and in the parafalcine regions.
Conclusion: Our study demonstrates that an AI solution can help radiologists to diagnose ICH and thus decrease the error rate. AI solution can serve as a prospective peer review tool for non-contrast head CT scans to identify ICH and thus minimize false negatives.
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Digital Pathology

Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers
The model is capable of solving multiple classification tasks and can satisfactorily classify glioma sub-types. The system provides a novel aid for the integrated neuropathological diagnostic workflow of glioma
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Other Computer Vision Applications

ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments
Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns.
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Natural Language Processing

EMR Summarization & Intelligence

Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke
ResultsΒ Among 262,875 individuals receiving neuroimaging, NLP identified 13,154 (5.0%) with SBI and 78,330 (29.8%) with WMD. The incidence of future stroke was 32.5 (95% confidence interval [CI] 31.1, 33.9) per 1,000 patient-years for patients with SBI: 19.3 (95% CI 18.9, 19.8) for patients with WMD and 6.8 (95% CI 6.7, 7.0) for patients without SCD. The crude hazard ratio (HR) associated with SBI was 3.40 (95% CI 3.25 to 3.56) and for WMD 2.63 (95% CI 2.54 to 2.71). With MRI-discovered SBI, the adjusted HR was 2.95 (95% CI 2.53 to 3.44) for those <65 years of age and 2.15 (95% CI 1.91 to 2.41) for those β‰₯65. With CT scan, the adjusted HR was 2.48 (95% CI 2.19 to 2.81) for those <65 and 1.81 (95% CI 1.71 to 1.91) for those β‰₯65. The adjusted HR associated with a finding of WMD was 1.76 (95% CI 1.69 to 1.82) and was not modified by age or imaging modality
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Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study
Results:Β The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion.
Conclusions:Β Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance
Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability.
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Chatbots

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Analytics

Signal Processing

Data Visualization

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A Living Pocketbook of Neurology
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