ArticleEuroasian journal of hepato-gastroenterology
Role of Dual Lens Endo-proctoscopy in Anorectal Disease.
Article in Euroasian journal of hepato-gastroenterology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aim and background: Anorectal diseases constitute a significant portion of surgical practice worldwide, affecting quality of life and productivity. Accurate diagnosis is essential for appropriate management, with proctoscopy being a fundamental diagnostic tool. This study aimed to evaluate the role of dual lens endo-proctoscopy (DLP) in the diagnosis and management of anorectal diseases. Materials and methods: This study was conducted at Shri BM Patil Medical College, Hospital, and Research Centre, Vijayapura, Karnataka, from April 2023 to January 2025. A total of 384 patients presenting with anorectal symptoms underwent evaluation using DLP. Results: The majority of patients belonged to the age-group of 31-40 years (27.6%) with a male predominance (71.4%). Anal fissure (20.3%), grade II hemorrhoids (19.8%), and fistula in ano (15.6%) were the predominant diagnoses. The images were utilized for patient counseling, histopathological reporting annotation, and to create image bank for artificial intelligence (AI)-based diagnosis of anorectal disease. Conclusion: Dual lens endo-proctoscopy demonstrated significant utility in the evaluation of anorectal diseases, offering enhanced visualization that facilitated accurate diagnosis and appropriate management planning. The technology's integration into routine clinical practice appears justified based on its diagnostic advantages, and for future use in AI-based diagnosis of anorectal disease. Clinical significance: The DLP offers a clearer and more detailed view of the anal canal, helping doctors spot issues like fissures, piles, and fistulas more accurately. Its side camera picks up details that older tools often miss, leading to better treatment planning. Plus, the images can be used to explain conditions to patients and train AI systems to improve future diagnosis. How to cite this article: Mathias LP, Chavan D, Patil VL,
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