OctopusNet: Machine learning for intelligent management of surgical tools

Rodrigues, Mark; Mayo, Michael; Patros, Panos

doi:10.1016/j.smhl.2021.100244

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Further, all the datasets surveyed in our paper have a flat structure. Given that fact that surgery is organised along specialities (Table 7), and each speciality has separate underlying categories, a hierarchical classification of surgical tools in the datasets provided for machine learning research has been shown to be extremely valuable (Rodrigues et al, 2022(Rodrigues et al, , 2021a.…”

Section: Dataset Volume Variety and Qualitymentioning

confidence: 99%

Surgical Tool Datasets for Machine Learning Research: A Survey

2022

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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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Section: Dataset Volume Variety and Qualitymentioning

confidence: 99%

Surgical Tool Datasets for Machine Learning Research: A Survey

2022

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“…Overall, the adoption of computer-assisted solutions to support in dentistry and other medical areas is not a novelty, with many approaches being described in recent years comprising different strategies [2,[19][20][21][22][23]. With new advances in sensory technologies and image processing algorithms, the available computational resources have increased considerably in the last years, but image processing techniques have been usually favored due to their lower implementation and computational costs in many scenarios [24,25].…”

Section: Related Workmentioning

confidence: 99%

A Computer-Assisted Approach to Assess the Precision of the Reciprocating Angles and the Rotation Speeds of Endodontic Motors

Rodrigues

Costa

Junior

et al. 2022

ASI

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Endodontic reciprocating motors are largely used to support a series of procedures in dentistry treatments, useful for those different circular movement patterns. In the last years, different motors have been available on the market, with varying costs and promised performance for the users; however, since their reciprocating angles and rotation speeds may have significant outcomes on the employed endodontic files, there should be an affordable and practical way to assess the actual performance of such motors concerning their expected operation. Actually, endodontic files attached to reciprocating motors will move too fast to be easily accounted, which has fostered the development of computational methods to allow the proper validation of their movements according to their official datasheets. In this scenario, this paper describes a feasible method to detect the movement patterns of different reciprocating endodontic motors by the processing of a set of consecutive images taken by a high-speed camera. The performed experiments for three different off-the-shelf reciprocating endodontic motors showed that their actual movement characteristics are slightly different from their specifications, and that each considered motor has a particular movement pattern.

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