Leveraging active learning techniques for surgical instrument recognition and localization

Abstract

The field of ophthalmic surgery demands accurate identification of specialized surgical instruments. Manual recognition can be time-consuming and prone to errors. In recent years neural networks have emerged as promising techniques for automating the classification process. However, the deployment of these advanced algorithms requires the collection of large amounts of data and a painstaking process of tagging selected elements. This paper presents a novel investigation into the application of neural networks for the detection and classification of surgical instruments in ophthalmic surgery. The main focus of the research is the application of active learning techniques, in which the model is trained by selecting the most informative instances to expand the training set. Various active learning methods are compared, with a focus on their effectiveness in reducing the need for significant data annotation – a major concern in the field of surgery. The use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to achieve high performance in the task of surgical tool detection is outlined. The combination of artificial intelligence (AI), machine learning, and Active Learning approaches, specifically in the field of ophthalmic surgery, opens new perspectives for improved diagnosis and surgical planning, ultimately leading to an improvement in patient safety and treatment outcomes.