%0 Journal Article
%T Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning.
%A Li YP
%A Lu TY
%A Huang FR
%A Zhang WM
%A Chen ZQ
%A Guang PW
%A Deng LY
%A Yang XH
%J World J Gastroenterol
%V 30
%N 10
%D 2024 Mar 14
%M 38596500
%F 5.374
%R 10.3748/wjg.v30.i10.1377
%X <B>BACKGROUND: </B>Crohn's disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed.<BR><B>OBJECTIVE: </B>To develop a method to identify CD and ITB with high accuracy, specificity, and speed.<BR><B>METHODS: </B>A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.<BR><B>RESULTS: </B>The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB.<BR><B>CONCLUSIONS: </B>Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.