The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review

Chang, Min Cheol; Kim, Jeoung Kun; Park, Donghwi; Kim, Jang Hwan; Kim, Chung Reen; Choo, Yoo Jin

doi:10.3390/healthcare11192687

Cited by 3 publications

References 46 publications

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Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis

Kim,

Chang

2024

Sci Rep

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Little is known about the therapeutic outcomes of transforaminal epidural steroid injection (TFESI) in patients with lumbosacral radicular pain due to lumbar spinal stenosis (LSS). Using lumbar spine radiographs as input data, we trained a convolutional neural network (CNN) to predict therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain caused by LSS. We retrospectively recruited 193 patients for this study. The lumbar spine radiographs included anteroposterior, lateral, and bilateral (left and right) oblique views. We cut each lumbar spine radiograph image into a square shape that included the vertebra corresponding to the level at which the TFESI was performed and the vertebrae juxta below and above that level. Output data were divided into “favorable outcome” (≥ 50% reduction in the numeric rating scale [NRS] score at 2 months post-TFESI) and “poor outcome” (< 50% reduction in the NRS score at 2 months post-TFESI). Using these input and output data, we developed a CNN model for predicting TFESI outcomes. The area under the curve of our model was 0.920. Its accuracy was 87.2%. Our CNN model has an excellent capacity for predicting therapeutic outcomes after lumbar TFESI in patients with lumbosacral radicular pain induced by LSS.

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Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis

Kim,

Chang

2024

Sci Rep

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Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation

Finocchi,

Campagnini,

Mannini

et al. 2024

Sci Rep

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Digital Transformation in Healthcare - A Recent Progress and Applications of Artificial Intelligence in Healthcare (Preprint)

Nadeem,

Rashid,

Zafar

et al. 2024

Preprint

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UNSTRUCTURED Artificial Intelligence (AI) is revolutionizing various healthcare sectors, enhancing diagnostic accuracy, patient care, and operational efficiency. However, there is a need for more involvement from healthcare stakeholders in promoting patient access, care, and compliance with digital therapeutics. This study aims to comprehensively analyze AI applications across different healthcare settings, including hospitals, dentistry, physical therapy, pharmacies, nursing, and medical laboratories. The study conducted a thorough literature review, using keywords to examine relevant articles from PubMed/Medline, Scopus, and Google Scholar databases. The review content is not restricted by time but is limited to English-language articles. The study found a significant need for AI facilitation in dentistry, nursing, and midwifery. AI solutions tailored to these fields can benefit patient outcomes, streamline workflows, and reduce the burden on healthcare providers. The study also explores the extensive use of AI in advanced healthcare fields such as diagnostics, genetics, and forensics. It then compares AI with other digital health technologies in terms of data utilization, integration, clinical support decisions, and impact on patient care. Finally, it outlines future aspects that underscore the need for customized training programs, infrastructure development, and robust ethical and legal frameworks to guide the adoption of AI in healthcare settings, ensuring informed consent, patient privacy and data security.

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The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review

Cited by 3 publications

References 46 publications

Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis

Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis

Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation

Digital Transformation in Healthcare - A Recent Progress and Applications of Artificial Intelligence in Healthcare (Preprint)

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