Artificial Intelligence Based Detection of Pneumoperitoneum on CT Scans in Patients Presenting with Acute Abdominal Pain: a Clinical Diagnostic Test Accuracy Study

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.

Enhancing Radiological Diagnosis: A Comprehensive Review of Image Quality Assessment and Optimization Strategies

Image quality plays a pivotal role in the accurate diagnosis and effective management of diseases in radiology. This review explores the principles, methodologies, and strategies for assessing and optimizing image quality across various imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine. We discuss key factors influencing image quality, such as spatial resolution, noise, contrast, and artifacts, and highlight techniques for quality assurance, image optimization, and dose reduction in clinical practice.

Applications of Artificial Intelligence in Acute Abdominal Imaging

Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.

A bump in the night: a 15-year retrospective analysis of urgent inpatient and emergency CT reporting out of hours in a tertiary referral centre

AIM

To assess the impact on specialty trainee (ST) experience of out-of-hours (OOH) working, focusing on what might be improved with both patient safety and staff wellbeing in mind.

MATERIALS AND METHODS

The number of acute computed tomography (CT) examinations reported OOH over the last 15 years (2007-2021) at Oxford University Hospitals NHS Foundation Trust was analysed. Qualitative data from the radiology STs participating in the acute OOH rotas were obtained using questionnaires during winter months in 2019 and 2021, before and after the introduction of an OOH CT outsourcing service in 2020.

RESULTS

Overnight acute CT has increased over 10-fold over the last decade to almost 50 CT examinations in 2021, and similar increases were observed during evening and weekend shifts. The option to outsource acute CT on an ad hoc basis was introduced in 2020 to manage the increase in demand. This resulted in a statistically significant improvement in the STs' level of reported satisfaction for OOH shifts (p<0.018), despite significantly increased perception of how busy the shifts were (p<0.035).

CONCLUSION

OOH acute CT reporting at Oxford NHS Foundation Trust has increased dramatically over the previous 15 years. Working patterns and resources have changed incrementally to absorb this increase in demand, most recently with the option for outsourcing at times of peak demand. The trend for increasing OOH CT demand has considerable implications for future resource planning.