This paper is a comprehensive survey of datasets for surgical tool detection and related surgical data science and machine learning techniques and algorithms. The survey offers a high level perspective of current research in this area, analyses the taxonomy of approaches adopted by researchers using surgical tool datasets, and addresses key areas of research, such as the datasets used, evaluation metrics applied and deep learning techniques utilised. Our presentation and taxonomy provides a framework that facilitates greater understanding of current work, and highlights the challenges and opportunities for further innovative and useful research.

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A new hierarchically organised dataset for artificial intelligence and machine learning research is presented, focusing on intelligent management of surgical tools. In addition to 360 surgical tool classes, we create a four level hierarchical structure for our dataset defined by 2 specialities, 12 packs and 35 sets. We employ different convolutional neural network training strategies to evaluate image classification and retrieval performance on this dataset, including the utilisation of prior information in the form of a taxonomic hierarchy tree structure. We evaluate the effects of image size and the number of images per class on model predictive performance. Experiments with the mapping of image features and class embeddings in semantic space using measures of semantic similarity between classes show that providing prior information results in a significant improvement in image retrieval performance on our dataset.

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This paper presents a novel convolutional neural network for multi-level classication of surgical tools, with a set of property predictions. Predictions are obtained from multiple levels of the model, and high accuracy is obtained by adjusting the depth of layers selected for predictions. Our architecture improves interpretability by providing a comprehensive set of predictions for each tool, allowing users to make rational decisions about whether to trust the model based on multiple pieces of information. These predictions can be evaluated against each other for consistency and error-checking. Important contributions of our work are the interpretable multi-level architecture, a novel surgical tool dataset, and a surgery knowledge base. This architecture provides a viable solution for intelligent management of surgical tools in a hospital, potentially leading to signicant cost savings and increased eciencies.