An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery DOI
Sina Saadati, Maryam Hashemi

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Abstract Artificial intelligence (AI) and computer vision are revolutionizing numerous fields, including robotic surgery, which stands to benefit immensely from advances in machine learning methodologies. While prior research has extensively focused on disorder detection, localization, semantic segmentation, the crucial challenge of arm maneuvering during autonomous surgeries remains underexplored. This study proposes a robust interpretable approach enable robots autonomously execute endometriosis by skillfully navigating their arms, equipped with camera surgical tools such as graspers or lasers. A decision tree framework is developed assess robot's real-time status guide its actions at every stage. integrates diverse ensemble neural network models for classification segmentation support decision-making. Specifically, proposed utilize deep image quality, identify obstructions caused adhesions, detect anatomical targets (e.g., uterus peritoneum), determine proximity ovary uterus. The further enhance accuracy detecting localizing ovary. By employing these frameworks within model, this work aims advance surgery capabilities, enabling fully autonomous, reliable, efficient operations. Consequently, method minimize economic costs, bleeding, post-operative pain, infection risk, while optimizing precision performance.

Language: Английский

An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery DOI
Sina Saadati, Maryam Hashemi

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Abstract Artificial intelligence (AI) and computer vision are revolutionizing numerous fields, including robotic surgery, which stands to benefit immensely from advances in machine learning methodologies. While prior research has extensively focused on disorder detection, localization, semantic segmentation, the crucial challenge of arm maneuvering during autonomous surgeries remains underexplored. This study proposes a robust interpretable approach enable robots autonomously execute endometriosis by skillfully navigating their arms, equipped with camera surgical tools such as graspers or lasers. A decision tree framework is developed assess robot's real-time status guide its actions at every stage. integrates diverse ensemble neural network models for classification segmentation support decision-making. Specifically, proposed utilize deep image quality, identify obstructions caused adhesions, detect anatomical targets (e.g., uterus peritoneum), determine proximity ovary uterus. The further enhance accuracy detecting localizing ovary. By employing these frameworks within model, this work aims advance surgery capabilities, enabling fully autonomous, reliable, efficient operations. Consequently, method minimize economic costs, bleeding, post-operative pain, infection risk, while optimizing precision performance.

Language: Английский

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