使用隨機森林實現超音波多特徵脂肪肝疾病分類

Abstract

近年來肝病已成台灣主要關注的疾病之一，並且其最大的風險因素-脂肪肝也漸漸被國人所重視，脂肪肝早期都是處於可逆的病變，但是若演變為肝纖維化後期，甚至會導致肝硬化，所以早期的診斷與治療特別的重要，目前病理切片為脂肪肝診斷之黃金標準，但因為其是侵入式的診斷方式，臨床上並不容易實施，而在其他影像診斷方式中，超音波因為其非侵入性、無放射性、可重複使用、以及方便操作與價格低廉等等原因，目前已成為臨床上診斷脂肪肝的最佳方式。 但是由於超音波及時成像的特性，在操作上需要有訓練有素的人員，並且觀察者之間的經驗差距會導致不同的超音波診斷結果，因此產生了定量式超音波，本研究旨在使用三個代表不同意義的超音波特徵，分別是夏農熵(Shannon entropy, SE)，代表了肝實質在超音波影像中的微結構變化；衰退係數(Attenuation coefficient, AE)，為一個可以量化超音波在介質中衰退狀況的係數；集成逆散射(Integrated backscatter, IB)，則是一個可以表示平均功率的函數，此三個超音波特徵結合醫師在臨床常使用用來判斷脂肪肝的三個特徵，身高體重指數(Body Mass Index, BMI)與天冬氨酸氨基轉移酶(Aspartate Transaminase, AST)、谷丙轉氨酶(Alanine transaminase, ALT)，來輔助醫師診斷脂肪肝。 本研究利用機器學習中的隨機森林演算法，結合上述所提到的六個特徵來訓練隨機森林模型判斷出5%脂肪肝病人以及33%脂肪肝病人，最終在5%的二分類模型上達到了80.3%的準確度，而33%二分類模型更是達到了90.1%的準確度。而在三分類上，直接訓練一個三分類模型的準確度達到了68.8%，而利用兩個二分類模型所達到的三分類效果則可以提升到72.1%。

In recent years, liver disease has become one of the main diseases of Taiwan, and its priority risk factor fatty liver disease is gradually taken more seriously. Fatty liver disease is in a reversible path in the early stage, but if it goes into the later stage of fibrosis, it may even cause cirrhosis, therefore early diagnosis and treatment are particularly important. The current gold standard for fatty liver diagnosis is liver biopsy. However, it is impractical as a diagnostic tool for it is an invasive diagnostic method. In other imaging methods, ultrasound is the best way to diagnose fatty liver because of its non-invasive, non-radioactive, reusable and low cost. However, due to the characteristics of ultrasonic imaging in time, it is necessary to have well-trained personnel in operation and the experience gap between observers will lead to different ultrasonic diagnosis results. Thus quantitative ultrasonic method was created. This study aims to use three ultrasound features representing different meanings are Shannon entropy (SE), which represents the microstructure change of the liver parenchyma in the ultrasound image; the attenuation coefficient (AE) is a quantifiable coefficient of the attenuation of the sound wave in the medium; Integrated backscatter (IB) is a function that can represent the average power. These three ultrasound features are combined with the three characteristics commonly used by doctors to determine fatty liver. Body Mass Index (BMI) and Aspartate Transaminase (AST), Alanine transaminase (ALT), to assist doctors in the diagnosis of fatty liver. This study used random forest algorithm in machine learning, combined with the six features mentioned above to train a random forest model to determine 5% fatty liver patients and 33% fatty liver patients, and finally reached a 5% binary classification model 80.3% accuracy and the 33% binary classification model achieved an accuracy of 90.1%. In the three classifications, the accuracy of directly training a multiclass model reached 68.8%, while the accuracy of the multiclass model by using successive dichotomies can be improved to 72.1%.