Application of machine learning algorithms for predicting internal carotid artery stenosis and comparing their value to duplex Doppler ultrasonography criteria

Yıl 2022, Cilt: 15 Sayı: 2, 213 - 222, 01.04.2022

Pınar Çeltikçi , Önder Eraslan Mehmet Atıcı Işık Conkbayır , Onur Ergun , Hasanali Durmaz , Emrah Çeltikçi

https://doi.org/10.31362/patd.956280

Cited By: 1

Öz

Purpose: There is a discrepancy between duplex Doppler ultrasonography (DUS) and digital subtraction angiography (DSA) for determining internal carotid artery (ICA) stenosis. We aim to train machine learning algorithms (MLAs) with DUS velocity values for predicting ICA stenosis and comparing their success to DUS criteria.
Materials and methods: DUS values (peak systolic velocity (PSV) and end-diastolic velocity of the common carotid artery (CCA) and ICA) and DSA studies of 159 ICA stenoses were reviewed retrospectively. Stenoses were classified as <50%, 50-69%, ≥70% by each modality. Linear regression models with descriptive and predictive analysis and MLAs; LightGBM, XgBoost, KNeighbors, Support Vector Machine (SVM), Decision Tree, Random Forest were trained with DUS values for predicting DSA stenosis.
Results: Predicted values of regression models have a linear relationship with DSA stenosis between 0-60%. LightGBM and SVM achieved the highest classification accuracy (69%), while all algorithms failed in the 50-69% interval. DUS criteria outperformed all MLAs in predicting DSA stenosis of ≥70% (sensitivity:0.91). Both MLAs and DUS criteria were unsuccessful in the 50-69% interval where DUS mostly overestimates and MLAs underestimate. MLAs using ICA PSV/CCA PSV ratio had higher accuracy for predicting DSA stenosis <50%.
Conclusion: DUS criteria could be considered as the sole diagnostic tool for ICA stenosis over 70%. Improved DUS criteria or wider training datasets for MLAs are warranted to detect 50-69% stenosis accurately.

Anahtar Kelimeler

Carotid artery, duplex Doppler ultrasonography, digital subtraction angiography, machine learning, stenosis

İnternal karotid arter darlığını tahmin etmede makine öğrenme algoritmalarının kullanımı ve öngörüm başarısının dubleks Doppler ultrasonografi kriterleriyle karşılaştırılması

Öz

Amaç: İnternal karotid arter (İKA) darlığını belirlemede, dupleks Doppler ultrasonografi (DUS) ile dijital subtraksiyon anjiyografi (DSA) arasında tutarsızlık bildirilmiştir. DUS hız değerleri ile eğitilmiş makine öğrenme algoritmalarının (MÖA), İKA darlığını tahmin etme performansını araştırmayı amaçlıyoruz.
Gereç ve yöntem: İKA darlığı olan 159 karotid bifurkasyonunun, ortak karotid arter (OKA) ve İKA'dan elde olunmuş DUS hız değerleri (pik sistolik hız (PSH) ve diyastol sonu hızı) ve DSA tetkikleri retrospektif olarak incelendi. Darlık derecesi her modaliteye göre <%50, %50-69, ≥%70 olarak sınıflandırıldı. Tanımlayıcı ve kestirimci analizler içeren doğrusal regresyon modelleri ve çeşitli MÖA’lar (LightGBM, XgBoost, KNeighbors, Support Vector Machine (SVM), Decision Tree, Random Forest) DSA’da saptanan darlık derecesini tahmin etmek için DUS hız değerleri ile eğitildi.
Bulgular: Regresyon modellerinin tahmin ettiği darlık değerleri ve asıl DSA darlık değerleri, %0-60 arasında doğrusal bir ilişkiye sahipti. MÖA’lar arasında LightGBM ve SVM en yüksek sınıflandırma doğruluğunu (%69) elde ederken, tüm algoritmalar %50-69 darlık aralığında başarısız oldu. DUS kriterleri, ≥%70'lik DSA darlığını tahmin etmede tüm MÖA’lardan daha iyi performans gösterdi (duyarlılık:0,91). Hem MÖA'lar hem de DUS kriterleri %50-69 darlık  aralığında başarısız olup, DUS darlığı olduğundan fazla, MÖA’lar darlığı olduğundan az
olarak tahmin etti. İKA PSH/OKA PSH oranını kullanan MÖA’lar, <%50 DSA darlığını öngörmede daha yüksek doğruluğa sahipti. 

Sonuç: DUS kriterleri, %70'in üzerinde İKA darlığı için tek tanı aracı olarak kabul edilebilir. Geliştirilmiş DUS kriterleri veya MÖA'lar için daha geniş eğitim veri setleri sağlanması, %50-69 darlık aralığının daha yüksek doğrulukla tespit edilmesini sağlayabilir.

Anahtar Kelimeler

Darlık, dijital subtraksiyon anjiyografi, dupleks Doppler ultrasonografi, karotid arter, makine öğrenmesi

Kaynakça

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