Fully Automatic Planning of Total Shoulder Arthroplasty Without Segmentation: A Deep Learning Based Approach

Kulyk, Paul; Vlachopoulos, Lazaros; Fürnstahl, Philipp; Zheng, Guoyan

doi:10.1007/978-3-030-11166-3_3

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…Yet, these tools often ignore the effect of softtissue in the simulation process and still require very labour intensive work for the surgical team to design patient-specific plans. Experts systems capable of automatically optimising the surgical plan for a given orthopedic surgery are now being developed [23] and promise to make surgical planning more efficient [24]. In the context of deep brain insertion of instruments, machine learning approaches capable of automatically planning trajectories of multiple instruments, to maximise the efficacy of the surgery while minimising intraoperative risks and avoiding collisions between instruments have demonstrated a significant reduction in planning time for the implantation of stereo-electroencephalography electrodes for epilepsy treatment [25] and for laser interstitial thermal therapy [26].…”

Section: Importance Of Flexible Contextual Machine Learningmentioning

confidence: 99%

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

et al. 2020

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Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer assisted interventions, we highlight the crucial need to take context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or CAI4CAI, arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision making ultimately producing more precise and reliable interventions.Index Terms-Artificial intelligence, computer assisted interventions, interventional workflow, intra-operative imaging, surgical planning, data fusion, surgical scene understanding, contextaware user interface, machine and deep learning, surgical data science

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Section: Importance Of Flexible Contextual Machine Learningmentioning

confidence: 99%

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

et al. 2020

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“…Kulyk et al [16] proposed a method for automatic detection of the articular marginal plane (AMP). The method used a deep learning approach without segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Role of Machine Learning and Deep Learning Techniques for Preoperative Prediction in Shoulder Arthroplasty: Survey

Tote¹,

Sonekar²

2021

Computational Intelligence in Pattern Recognition

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In the last few years, rapid acceptance and improvement of technology have been seen in shoulder arthroplasty practices. The success rate of shoulder arthroplasty can be improved by taking advantages of artificial intelligence for finding preoperative parameters and better decision-making. The primary identification and the selection of which sort of arthroplasty is employed have relied on the indications like inflammatory arthritis and humerus fractures. Different artificial intelligence techniques used and their impact on the prediction of shoulder arthroplasty are discussed in our paper. We review the artificial intelligence-based techniques in terms of speculative and investigational work carried out in the literature.

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“…Furthermore, AI has been used for implant placement in other areas of the body. In a study performed on 27 subjects, a fully convolutional deep learning model was used to determine the position and orientation of the articular marginal plane of the proximal humerus based on CT scans ( Kulyk, Vlachopoulos et al 2018 ). Carrillo et al (2017) generated, in a fully automatic manner, a surgical plan for corrective osteotomies of malunited radius bones ( Carrillo, Vlachopoulos et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

Alsomali

Alghamdi

Alotaibi

et al. 2022

The Saudi Dental Journal

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Fully Automatic Planning of Total Shoulder Arthroplasty Without Segmentation: A Deep Learning Based Approach

Cited by 6 publications

References 14 publications

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

A Role of Machine Learning and Deep Learning Techniques for Preoperative Prediction in Shoulder Arthroplasty: Survey

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

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