Künstliche Intelligenz in der Neurointensivmedizin

Schweingruber, Nils; Gerloff, Christian

doi:10.1007/s00115-020-01050-4

Cited by 3 publications

(2 citation statements)

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“…The complexity matrix shown in Table 2 is used to calculate performance metrics (Table 2). 2 True Negative, 3 False Positive, 4 The complexity matrix obtained as a result of the study is shown in Figure 3. In this matrix, each algorithm is classified into 4 different groups as the CTS grade of the algorithm (0 normal, 1 mild, 2 modarete, 3 severe).…”

Section: Simulation Results and Performance Evaluationmentioning

confidence: 99%

“…2 AI aims to develop a method for capturing and solving complex problems based on large amounts of data. 3 It also has the effect of changing the current model in the diagnosis, treatment, prediction, and economics of neurological diseases. 2 It has been understood that with the use of artificial intelligence, it becomes easier to diagnose permanent neurological damage and even guides the prevention of diseases.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Based Rating of Carpal Tunnel Syndrome Efficacy in Clinical Diagnosis

SARICA DAROL,

ECE,

UZUN

et al. 2023

Acta Med Nicomedia

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Objective: The most common entrapment neuropathy seen by the clinician is Carpal tunnel syndrome (CTS). CTS is graded as mild, moderate, and severe according to the results obtained on electroneuromyography (ENMG) by clinicians. We aimed to show the effectiveness of the use of artificial intelligence in clinical diagnosis in the grading of CTS. Methods: In our study, the data of 315 people with a pre-diagnosis of CTS were used and classified into four classes based on AI as CTS grade. Machine Learning (ML) algorithms Ensemble, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (Tree) algorithms were used in classification processes. 10% Hold-out validation was used and the learning rate was determined as 0.1. As a result of the classification, accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained. Results: SVM made the best estimation and KNN made the worst estimation in the 0 class. The best estimate in class 1 belongs to the Support Vector Machine. Ensemble and Tree made the best guesses in the 2nd and 3rd grades. In our study, the best algorithm with an overall success rate is SVM with 93.55%. Conclusions: The results showed that ML algorithm models consistently provided better predictive results and would assist physicians in determining the medical treatment modality of CTS. Artificial intelligence (AI) techniques are reliable methods that assist clinicians to deliver quality healthcare.

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Section: Simulation Results and Performance Evaluationmentioning

confidence: 99%