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

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

Makale detayları

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

Yazarlar:

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

Yayıncı:

Kocaeli University

Ana Başlıklar:

Nöroloji ve Nöromüsküler Hastalıklar

Dergi:

Acta Medica Nicomedia Vol.6 Issue 2, pp.213-219

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292869

pISSN:

2717-8994

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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 KTSnin tıbbi tedavi yöntemini belirlemede yardımcı olacağını gösterdi. Yapay zeka teknikleri, klinisyenlerin kaliteli sağlık hizmeti sunmalarına yardımcı olan güvenilir yöntemlerdir.

Makale detayları

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

Yazarlar:

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

Yayıncı:

Kocaeli University

Ana Başlıklar:

Nöroloji ve Nöromüsküler Hastalıklar

Dergi:

Acta Medica Nicomedia Vol.6 Issue 2, pp.213-219

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292869

pISSN:

2717-8994

carpal tunnel syndrome

electromyography

artificial intelligence

gradingkarpal tünel sendromu

elektromiyografi

yapay zeka

derecelendirme

Sayı

Makale detayları

Yazarlar: Elif Sarıca Darol | Yıldız Ece | Süleyman Uzun | Murat Alemdar

Yayıncı: Kocaeli University

Ana Başlıklar: Nöroloji ve Nöromüsküler Hastalıklar

Dergi: Acta Medica Nicomedia Vol.6 Issue 2, pp.213-219

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001292869

pISSN: 2717-8994

Sayı

Yazarlar:

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

Yayıncı:

Kocaeli University

Ana Başlıklar:

Nöroloji ve Nöromüsküler Hastalıklar

Dergi:

Acta Medica Nicomedia Vol.6 Issue 2, pp.213-219

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292869

pISSN:

2717-8994