Enhancing Safety During Surgical Procedures with Computer Vision, Artificial Intelligence, and Natural Language Processing

Administering the incorrect substances during procedures can lead to undesired outcomes. Anesthetists, especially when fatigued, distracted, or under stressful conditions, are at risk of such oversights. The conventional manual method for identifying, verifying, and preparing such substances has inherent challenges. Addressing these issues is paramount for healthcare professionals and those under their care. This study explores the application of computer vision and artificial intelligence techniques to refine the processes of selection, verification, preparation, and dispensing in procedure settings. The advanced method initiates with scene text detection, extraction, and matching models to discern inscriptions on labels. These inscriptions are subsequently matched with a pre-established database of item attributes using the token set ratio, Levenshtein, and Jaccard distance algorithms. These algorithms provide similarity scores, with the item having the top confidence score being identified as the appropriate one. A groundbreaking facet of this approach is the incorporation of a generalized Ukkonen algorithm, upper bound theory, and branch pruning algorithm. These elements offer an enhanced and more accurate adaptation of the traditional Levenshtein algorithm. Utilizing this method has the potential to drastically diminish errors in procedures and elevate safety measures.