Adhesion-related small bowel obstruction: deep learning for automatic transition-zone detection by CT

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.

Deep learning for automatic bowel-obstruction identification on abdominal CT

RATIONALE AND OBJECTIVES

Automated evaluation of abdominal computed tomography (CT) scans should help radiologists manage their massive workloads, thereby leading to earlier diagnoses and better patient outcomes. Our objective was to develop a machine-learning model capable of reliably identifying suspected bowel obstruction (BO) on abdominal CT.

MATERIALS AND METHODS

The internal dataset comprised 1345 abdominal CTs obtained in 2015-2022 from 1273 patients with suspected BO; among them, 670 were annotated as BO yes/no by an experienced abdominal radiologist. The external dataset consisted of 88 radiologist-annotated CTs. We developed a full preprocessing pipeline for abdominal CT comprising a model to locate the abdominal-pelvic region and another model to crop the 3D scan around the body. We built, trained, and tested several neural-network architectures for the binary classification (BO, yes/no) of each CT. F1 and balanced accuracy scores were computed to assess model performance.

RESULTS

The mixed convolutional network pretrained on a Kinetics 400 dataset achieved the best results: with the internal dataset, the F1 score was 0.92, balanced accuracy 0.86, and sensitivity 0.93; with the external dataset, the corresponding values were 0.89, 0.89, and 0.89. When calibrated on sensitivity, this model produced 1.00 sensitivity, 0.84 specificity, and an F1 score of 0.88 with the internal dataset; corresponding values were 0.98, 0.76, and 0.87 with the external dataset.

CONCLUSION

The 3D mixed convolutional neural network developed here shows great potential for the automated binary classification (BO yes/no) of abdominal CT scans from patients with suspected BO.

CLINICAL RELEVANCE STATEMENT

The 3D mixed CNN automates bowel obstruction classification, potentially automating patient selection and CT prioritization, leading to an enhanced radiologist workflow.

KEY POINTS

• Bowel obstruction's rising incidence strains radiologists. AI can aid urgent CT readings. • Employed 1345 CT scans, neural networks for bowel obstruction detection, achieving high accuracy and sensitivity on external testing. • 3D mixed CNN automates CT reading prioritization effectively and speeds up bowel obstruction diagnosis.

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

OBJECTIVE

Artificial intelligence (AI) could help medical practitioners in analyzing radiological images to determine the presence and site of bowel obstruction. This retrospective diagnostic study proposed a series of deep learning (DL) models for diagnosing bowel obstruction on abdominal radiograph.

METHODS

A total of 2082 upright plain abdominal radiographs were retrospectively collected from four hospitals. The images were labeled as normal, small bowel obstruction and large bowel obstruction by three senior radiologists based on comprehensive examinations and interventions within 48 hours after admission. Gradient-weighted class activation mapping was used to visualize the inferential explanation.

RESULTS

In the validation set, the Xception-backboned model achieved the highest accuracy (0.863), surpassing the VGG16 (0.847) and ResNet models (0.836). In the test set, the Xception model (accuracy: 0.807) outperformed other models and a junior radiologist (0.780) but not a senior radiologist (0.840). In the AI-aided diagnostic framework, the junior and senior radiologists made their judgements while aware of the Xception model predictions. Their accuracy significantly improved to 0.887 and 0.913, respectively.

CONCLUSIONS

We developed and validated DL-based computer vision models for diagnosing bowel obstruction on plain abdominal radiograph. DL-based computer-aided diagnostic systems could reduce medical practitioners' workloads and improve diagnostic accuracy.

Towards an EKG for SBO: A Neural Network for Detection and Characterization of Bowel Obstruction on CT

A neural network was developed to detect and characterize bowel obstruction, a common cause of acute abdominal pain. In this retrospective study, 202 CT scans of 165 patients with bowel obstruction from March to June 2022 were included and partitioned into training and test data sets. A multi-channel neural network was trained to segment the gastrointestinal tract, and to predict the diameter and the longitudinal position ("longitude") along the gastrointestinal tract using a novel embedding. Its performance was compared to manual segmentations using the Dice score, and to manual measurements of the diameter and longitude using intraclass correlation coefficients (ICC). ROC curves as well as sensitivity and specificity were calculated for diameters above a clinical threshold for obstruction, and for longitudes corresponding to small bowel. In the test data set, Dice score for segmentation of the gastrointestinal tract was 78 ± 8%. ICC between measured and predicted diameters was 0.72, indicating moderate agreement. ICC between measured and predicted longitude was 0.85, indicating good agreement. AUROC was 0.90 for detection of dilated bowel, and was 0.95 and 0.90 for differentiation of the proximal and distal gastrointestinal tract respectively. Overall sensitivity and specificity for dilated small bowel were 0.83 and 0.90. Since obstruction is diagnosed based on the diameter and longitude of the bowel, this neural network and embedding may enable detection and characterization of this important disease on CT.

Deep learning using computed tomography to identify high-risk patients for acute small bowel obstruction: development and validation of a prediction model : a retrospective cohort study

OBJECTIVE

To build a novel classifier using an optimized 3D-convolutional neural network for predicting high-grade small bowel obstruction (HGSBO).

SUMMARY BACKGROUND DATA

Acute SBO is one of the most common acute abdominal diseases requiring urgent surgery. While artificial intelligence and abdominal computed tomography (CT) have been used to determine surgical treatment, differentiating normal cases, HGSBO requiring emergency surgery, and low-grade SBO (LGSBO) or paralytic ileus is difficult.

METHODS

A deep learning classifier was used to predict high-risk acute SBO patients using CT images at a tertiary hospital. Images from three groups of subjects (normal, nonsurgical, and surgical) were extracted; the dataset used in the study included 578 cases from 250 normal subjects, with 209 HGSBO and 119 LGSBO patients; over 38 000 CT images were used. Data were analyzed from 1 June 2022 to 5 February 2023. The classification performance was assessed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.

RESULTS

After fivefold cross-validation, the WideResNet classifier using dual-branch architecture with depth retention pooling achieved an accuracy of 72.6%, an area under receiver operating characteristic of 0.90, a sensitivity of 72.6%, a specificity of 86.3%, a positive predictive value of 74.1%, and a negative predictive value of 86.6% on all the test sets.

CONCLUSIONS

These results show the satisfactory performance of the deep learning classifier in predicting HGSBO compared to the previous machine learning model. The novel 3D classifier with dual-branch architecture and depth retention pooling based on artificial intelligence algorithms could be a reliable screening and decision-support tool for high-risk patients with SBO.

Visual Image Annotation for Bowel Obstruction: Repeatability and Agreement with Manual Annotation and Neural Networks

Bowel obstruction is a common cause of acute abdominal pain. The development of algorithms for automated detection and characterization of bowel obstruction on CT has been limited by the effort required for manual annotation. Visual image annotation with an eye tracking device may mitigate that limitation. The purpose of this study is to assess the agreement between visual and manual annotations for bowel segmentation and diameter measurement, and to assess agreement with convolutional neural networks (CNNs) trained using that data. Sixty CT scans of 50 patients with bowel obstruction from March to June 2022 were retrospectively included and partitioned into training and test data sets. An eye tracking device was used to record 3-dimensional coordinates within the scans, while a radiologist cast their gaze at the centerline of the bowel, and adjusted the size of a superimposed ROI to approximate the diameter of the bowel. For each scan, 59.4 ± 15.1 segments, 847.9 ± 228.1 gaze locations, and 5.8 ± 1.2 m of bowel were recorded. 2d and 3d CNNs were trained using this data to predict bowel segmentation and diameter maps from the CT scans. For comparisons between two repetitions of visual annotation, CNN predictions, and manual annotations, Dice scores for bowel segmentation ranged from 0.69 ± 0.17 to 0.81 ± 0.04 and intraclass correlations [95% CI] for diameter measurement ranged from 0.672 [0.490-0.782] to 0.940 [0.933-0.947]. Thus, visual image annotation is a promising technique for training CNNs to perform bowel segmentation and diameter measurement in CT scans of patients with bowel obstruction.

[Update: Small bowel diseases in computed tomography and magnetic resonance imaging]

CLINICAL/METHODICAL ISSUE

Radiological procedures play a crucial role in the diagnosis of small bowel disease. Due to a broad and quite nonspecific spectrum of symptoms, clinical evaluation is often difficult, and endoscopic procedures require significant manpower, are time-consuming and expensive. In contrast, radiologic imaging can provide important information about morphologic and functional variations of the small bowel and help to identify various disease entities, such as inflammation, tumors, vascular problems, and obstruction.

STANDARD RADIOLOGICAL METHODS

The most common radiological modalities in small bowel diagnostics include ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and fluoroscopy. Each of these modalities has its own advantages and limitations, and the choice of imaging modality depends on clinical symptoms and suspected diagnosis in addition to availability.

METHODOLOGICAL INNOVATIONS

In recent years, significant progress has been made, especially in cross-sectional imaging modalities, as a result of new and further technical developments.

PERFORMANCE

These range from increasing detail resolution to functional and molecular imaging techniques that go far beyond simple morphology. In addition, information technology (IT) applications, which include artificial intelligence and radiomics, are assuming an increasing role.

ACHIEVEMENTS

Many of the methods mentioned are still in early stages and need to be further developed for daily practice, but some have already found their way into clinical routine.

PRACTICAL RECOMMENDATIONS

The aim of this work is to provide a review of the most important disease entities of the small intestine, including new and innovative diagnostic approaches.

Evaluating temporal trends and the impact of surgical subspecialisation on patient outcomes following adhesional small bowel obstruction: a multicentre cohort study

PURPOSE

Small bowel obstruction (SBO) is the most common indication for laparotomy in the UK. While general surgeons have become increasingly subspecialised in their elective practice, emergency admissions commonly remain undifferentiated. This study aimed to assess temporal trends in the management of adhesional SBO and explore the influence of subspecialisation on patient outcomes.

METHODS

Data was collected for patients admitted acutely with adhesional SBO across acute NHS trusts in Northern England between 01/01/02 and 31/12/16, including demographics, co-morbidities and procedures performed. Patients were excluded if a potentially non-adhesional cause was identified and were grouped by the responsible consultant's subspecialty. The primary outcome of interest was 30-day inpatient mortality.

RESULTS

Overall, 2818 patients were admitted with adhesional SBO during a 15-year period. There was a consistent female preponderance, but age and comorbidity increased significantly over time (both p < 0.001). In recent years, more patients were managed operatively with a trend away from delayed surgery also evident (2002-2006: 65.7% vs. 2012-2016: 42.7%, p < 0.001). Delayed surgery was associated with an increased mortality risk on multivariable regression analysis (OR: 2.46 (1.46-4.23, p = 0.001)). CT scanning was not associated with management strategy or timing of surgery (p = 0.369). There was an increased propensity for patients to be managed by gastrointestinal (colorectal and upper gastrointestinal) subspecialists over time. Length of stay (p < 0.001) and 30-day mortality (p < 0.001) both improved in recent years, with the best outcomes seen in colorectal (2.6%) and vascular subspecialists (2.4%). However, following adjustment for confounding variables, consultant subspecialty was not a predictor of mortality.

CONCLUSION

Outcomes for patients presenting with adhesional SBO have improved despite the increasing burden of age and co-morbidity. While gastrointestinal subspecialists are increasingly responsible for their care, mortality is not influenced by consultant subspecialty.

Progress in diagnosis and treatment of surgery-related adhesive small intestinal obstruction

Adhesive small bowel obstruction is a relatively common surgical acute abdomen, which is caused by various factors that result in the contents of the small bowel failing to pass smoothly. The clinical symptoms include abdominal pain, distension, nausea and vomiting, and defecation disorder. The chance of adhesive small bowel obstruction to develop in patients with a history of abdominal surgery is around 2.4%. This paper discusses the most recent developments in the conservative and surgical management of adhesive small bowel obstruction based on clinical manifestation, laboratory analysis, and imaging examination.