Approach to Abdominal Imaging in 2022

Clinical issues and challenges in imaging of gastrointestinal diseases: A minireview and our experience

Imaging techniques play a crucial role in the modern era of medicine, particularly in gastroenterology. Nowadays, various non-invasive and invasive imaging modalities are being routinely employed to evaluate different gastrointestinal (GI) diseases. However, many instrumental as well as clinical issues are arising in the area of modern GI imaging. This minireview article aims to briefly overview the clinical issues and challenges encountered in imaging GI diseases while highlighting our experience in the field. We also summarize the advances in clinically available diagnostic methods for evaluating different diseases of the GI tract and demonstrate our experience in the area. In conclusion, almost all imaging techniques used in imaging GI diseases can also raise many challenges that necessitate careful consideration and profound expertise in this field.

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Background

Acute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.

Methods

In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.

Results

Eleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.

Conclusion

The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.