KARPAL TÜNEL SENDROMUNUN DÜZEYİNİN YAPAY ZEKA TEMELLİ DERECELENDİRİLMESİ

Year 2023, Volume: 6 Issue: 2, 213 - 219, 30.06.2023

Elif Sarıca Darol , Yıldız Ece , Süleyman Uzun , Murat Alemdar

https://doi.org/10.53446/actamednicomedia.1195719

Cited By: 2

Abstract

Amaç: Karpal tünel sendromu (KTS), median sinirin karpal tünelde sıkışması sonucu en sık görülen tuzak nöropatisidir. Elde edilen veriler sonucunda hastada mevcut KTS kliniği hafif, orta ve ağır olarak gradelenir. KTS derecelendirmesinde klinik tanıda yapay zeka kullanımının etkinliğini göstermeyi amaçladık.
Yöntem: Çalışmamızda KTS ön tanısı ile başvurmuş ve electroneuromyography yapılmış olan 315 bireyin, demografik ve electroneuromyography sonuçlarından elde edilmiş sinir ileti verileri kullanılmıştır. Sınıflandırma işlemlerinde makine öğrenmesi algoritmalarından Topluluk, Destek Vektör Makinesi, K-En Yakın Komşu ve Karar Ağacı algoritmaları kullanılmıştır. %10 bekletme doğrulaması kullanılmış ve öğrenme oranı 0.1 olarak belirlenmiştir. Sınıflandırma sonucunda doğruluk, kesinlik, duyarlılık, özgüllük ve F1-skor performans değerleri elde edilmiştir.
Bulgular: Çalışmamızın sonucunda 0 sınıfında en iyi tahmini Destek Vektör Makinesi, en kötü tahmini K-En Yakın Komşu yapmıştır. 1. sınıfda en iyi tahmin Destek Vektör Makinesine aittir. 2. ve 3. sınıflarda en iyi tahmini Topluluk ve Karar Ağacı yapmıştır. Çalışmamızda, genel başarı oranı en iyi algoritma % 93,55 ile Destek Vektör Makinesidir.
Sonuç: Makine öğrenme algoritma modellerinin tutarlı bir şekilde daha iyi tahmin sonuçları sağladığını ve doktorlara KTS'nin tıbbi tedavi yöntemini belirlemede yardımcı olacağını gösterdi. Yapay zeka teknikleri, klinisyenlerin kaliteli sağlık hizmeti sunmalarına yardımcı olan güvenilir yöntemlerdir.

Keywords

karpal tünel sendromu, elektromiyografi, yapay zeka, derecelendirme

ARTIFICIAL INTELLIGENCE BASED RATING OF CARPAL TUNNEL SYNDROME EFFICACY IN CLINICAL DIAGNOSIS

Abstract

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.

Keywords

carpal tunnel syndrome, electromyography, artificial intelligence, grading

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