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Objective <p>Predicting treatment response in Crohn’s disease (CD) is essential for making an optimal therapeutic regimen, but relevant models are lacking. This study aimed to develop a deep learning model based on baseline intestinal ultrasound (IUS) images and clinical information to predict mucosal healing.</p> Methods <p>Consecutive CD patients who underwent pretreatment IUS were retrospectively recruited at a tertiary hospital. A total of 1548 IUS images of longitudinal diseased bowel segments were collected and divided into a training cohort and a test cohort. A convolutional neural network model was developed to predict mucosal healing after one year of standardized treatment. The model’s efficacy was validated using the five-fold internal cross-validation and further tested in the test cohort.</p> Results <p>A total of 190 patients (68.9% men, mean age 32.3 ± 14.1 years) were enrolled, consisting of 1038 IUS images of mucosal healing and 510 images of no mucosal healing. The mean area under the curve in the test cohort was 0.73 (95% CI: 0.68–0.78), with the mean sensitivity of 68.1% (95% CI: 60.5–77.4%), specificity of 69.5% (95% CI: 60.1–77.2%), positive prediction value of 80.0% (95% CI: 74.5–84.9%), negative prediction value of 54.8% (95% CI: 48.0–63.7%). Heat maps showing the deep-learning decision-making process revealed that information from the bowel wall, serous surface, and surrounding mesentery was mainly considered by the model.</p> Conclusions <p>We developed a deep learning model based on IUS images to predict mucosal healing in CD with notable accuracy. Further validation and improvement of this model with more multi-center, real-world data are needed.</p> Critical relevance statement <p>Predicting treatment response in CD is essential to making an optimal therapeutic regimen. In this study, a deep-learning model using pretreatment ultrasound images and clinical information was generated to predict mucosal healing with an AUC of 0.73.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Response to medication treatment is highly variable among patients with CD.</p> </ItemContent> <ItemContent> <p>High-resolution IUS images of the intestinal wall may hide significant characteristics for treatment response.</p> </ItemContent> <ItemContent> <p>A deep-learning model capable of predicting treatment response was generated using pretreatment IUS images.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Predicting mucosal healing in Crohn’s disease: development of a deep-learning model based on intestinal ultrasound images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月16日

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  • Li Ma,
  • Yuepeng Chen,
  • Xiangling Fu,
  • Jing Qin,
  • Yanwen Luo,
  • Yuanjing Gao,
  • Wenbo Li,
  • Mengsu Xiao,
  • Zheng Cao,
  • Jialin Shi,
  • Qingli Zhu,
  • Chenyi Guo,
  • Ji Wu

摘要

Objective

Predicting treatment response in Crohn’s disease (CD) is essential for making an optimal therapeutic regimen, but relevant models are lacking. This study aimed to develop a deep learning model based on baseline intestinal ultrasound (IUS) images and clinical information to predict mucosal healing.

Methods

Consecutive CD patients who underwent pretreatment IUS were retrospectively recruited at a tertiary hospital. A total of 1548 IUS images of longitudinal diseased bowel segments were collected and divided into a training cohort and a test cohort. A convolutional neural network model was developed to predict mucosal healing after one year of standardized treatment. The model’s efficacy was validated using the five-fold internal cross-validation and further tested in the test cohort.

Results

A total of 190 patients (68.9% men, mean age 32.3 ± 14.1 years) were enrolled, consisting of 1038 IUS images of mucosal healing and 510 images of no mucosal healing. The mean area under the curve in the test cohort was 0.73 (95% CI: 0.68–0.78), with the mean sensitivity of 68.1% (95% CI: 60.5–77.4%), specificity of 69.5% (95% CI: 60.1–77.2%), positive prediction value of 80.0% (95% CI: 74.5–84.9%), negative prediction value of 54.8% (95% CI: 48.0–63.7%). Heat maps showing the deep-learning decision-making process revealed that information from the bowel wall, serous surface, and surrounding mesentery was mainly considered by the model.

Conclusions

We developed a deep learning model based on IUS images to predict mucosal healing in CD with notable accuracy. Further validation and improvement of this model with more multi-center, real-world data are needed.

Critical relevance statement

Predicting treatment response in CD is essential to making an optimal therapeutic regimen. In this study, a deep-learning model using pretreatment ultrasound images and clinical information was generated to predict mucosal healing with an AUC of 0.73.

Key Points

Response to medication treatment is highly variable among patients with CD.

High-resolution IUS images of the intestinal wall may hide significant characteristics for treatment response.

A deep-learning model capable of predicting treatment response was generated using pretreatment IUS images.

Graphical Abstract