Abstract
Analysis of operative data with convolutional neural networks (CNNs) is expected to improve the knowledge and professional skills of surgeons. Identification of objects in videos recorded during surgery can be used for surgical skill assessment and surgical navigation. The objectives of this study were to recognize objects and types of forceps in surgical videos acquired during colorectal surgeries and evaluate detection accuracy. Images (n = 1818) were extracted from 11 surgical videos for model training, and another 500 images were extracted from 6 additional videos for validation. The following 5 types of forceps were selected for annotation: ultrasonic scalpel, grasping, clip, angled (Maryland and right-angled), and spatula. IBM Visual Insights software was used, which incorporates the most popular open-source deep-learning CNN frameworks. In total, 1039/1062 (97.8%) forceps were correctly identified among 500 test images. Calculated recall and precision values were as follows: grasping forceps, 98.1% and 98.0%; ultrasonic scalpel, 99.4% and 93.9%; clip forceps, 96.2% and 92.7%; angled forceps, 94.9% and 100%; and spatula forceps, 98.1% and 94.5%, respectively. Forceps recognition can be achieved with high accuracy using deep-learning models, providing the opportunity to evaluate how forceps are used in various operations.
Highlights
Analysis of operative data with convolutional neural networks (CNNs) is expected to improve the knowledge and professional skills of surgeons
Deep learning is based on computer programs that automatically conduct repetitive learning from provided data and identify appropriate rules based on this p rocess[4,5]
The total number of forceps identified in 500 test images was 1062
Summary
Analysis of operative data with convolutional neural networks (CNNs) is expected to improve the knowledge and professional skills of surgeons. The objectives of this study were to recognize objects and types of forceps in surgical videos acquired during colorectal surgeries and evaluate detection accuracy. Artificial Intelligence (AI) has been extensively utilized in many fields[1] and has contributed tremendously to improvements and advancements of technology In this context, development using deep-learning technology[2,3] has shared in the contribution. As a first step in the analysis of surgical procedures, an object recognition model is required to identify objects in surgical videos that require surgical skill assessment and surgical navigation. We constructed a model to recognize the object and types of forceps in surgical videos acquired during colorectal surgeries and evaluated its accuracy
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