Clinical Scorecard: Automated Differentiation of Benign Anal and Sphincter Lesions Using Artificial Intelligence and Endoanal Ultrasound Techniques
At a Glance
Category
Detail
Condition
Benign anorectal disorders including anal fissures and sphincteric lacerations
Key Mechanisms
Structural anal sphincteric disease affecting external and internal anal sphincters; lesions detected as interruptions or breaks in sphincter echogenic rings on EAUS
Target Population
Patients with benign anorectal conditions across sexes and age groups, including asymptomatic women with obstetric injuries
Care Setting
Specialized gastroenterology and proctology clinics with access to endoanal ultrasonography and AI diagnostic tools
Key Highlights
Endoanal ultrasonography (EAUS) is the gold standard for evaluating anal sphincter integrity, outperforming MRI for internal anal sphincter defects.
EAUS has limitations including a steep learning curve, limited accessibility, and variability affecting reproducibility.
Artificial intelligence using a convolutional neural network (CNN) model can accurately classify anal fissures and lacerations on EAUS images, improving diagnostic accuracy and addressing specialist scarcity.
Guideline-Based Recommendations
Diagnosis
Use EAUS as the primary imaging modality for assessing sphincter integrity in benign anorectal disorders.
Incorporate AI-assisted analysis to enhance detection and classification of anal fissures and sphincter lacerations.
Management
Accurate identification of lesion type and location is essential to guide appropriate symptom relief and prevent complications.
Consider multidisciplinary evaluation including gastroenterologists trained in EAUS and AI tools.
Monitoring & Follow-up
Regular follow-up with EAUS may be warranted to assess sphincter healing or progression of lesions.
Utilize AI models to support consistent interpretation and reduce interobserver variability during monitoring.
Risks
Delayed or inaccurate diagnosis may lead to persistent symptoms and impaired quality of life.
Limited access to EAUS and trained personnel can hinder timely and accurate evaluation.
Patient & Prescribing Data
Patients undergoing evaluation for benign anal fissures and sphincter lacerations using EAUS
AI-assisted EAUS classification demonstrated high sensitivity and specificity, particularly 100% accuracy for fissures, supporting its use to inform clinical decision-making.
Clinical Best Practices
Ensure EAUS is performed by experienced operators to maximize image quality and diagnostic yield.
Adopt AI-based CNN models to assist in lesion classification, reducing the learning curve and variability among clinicians.
Maintain patient confidentiality and data anonymization during AI model training and application.
Use a multidisciplinary approach integrating clinical, imaging, and AI data for comprehensive patient assessment.
