{Reference Type}: Journal Article
{Title}: Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning.
{Author}: Li YP;Lu TY;Huang FR;Zhang WM;Chen ZQ;Guang PW;Deng LY;Yang XH;
{Journal}: World J Gastroenterol
{Volume}: 30
{Issue}: 10
{Year}: 2024 Mar 14
{Factor}: 5.374
{DOI}: 10.3748/wjg.v30.i10.1377
{Abstract}: <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.