Finding discriminative and interpretable patterns in sequences of surgical activities

Forestier, Germain; Petitjean, Franois; Senin, Pavel; Riffaud, Laurent; Henaux, Pierre-Louis; Jannin, Pierre

doi:10.1016/j.artmed.2017.09.002

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…Researchers built a framework to automatically identify practical patterns for discriminating different experience surgeon from surgery recordings. 93 …”

Section: Resultsmentioning

confidence: 99%

“…Researchers built a framework to automatically identify practical patterns for discriminating different experience surgeon from surgery recordings. 93 In rehabilitation of SCI patients, ML framework could support researchers and clinicians for selection of epidural stimulation parameters. 94 During electrically evoked contractions, SVM increased safety by adapting the functional electrical stimulation parameters in motor complete SCI individuals.…”

Section: Image Processing and Diagnosismentioning

confidence: 99%

See 1 more Smart Citation

Current Applications of Machine Learning in Spine: From Clinical View

Ren

Xie

et al. 2021

Global Spine Journal

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Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

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“…Researchers built a framework to automatically identify practical patterns for discriminating different experience surgeon from surgery recordings. 93 …”

Section: Resultsmentioning

confidence: 99%

Section: Image Processing and Diagnosismentioning

confidence: 99%

Current Applications of Machine Learning in Spine: From Clinical View

Ren

Xie

et al. 2021

Global Spine Journal

View full text Add to dashboard Cite

show abstract

“…After that, we measure the similarity between each Pareto optimal solution and the positive ideal solution using the cosine similarity measure as Eq. 11 [24,25]. Finally, we select the Pareto optimal solution with the highest similarity value as the best set of cluster centres which denotes the optimal clusters.…”

Section: Optimal Solutionsmentioning

confidence: 99%

Multi-objective semi-supervised clustering to identify health service patterns for injured patients

Khorshidi

Aickelin

Haffari

et al. 2019

Health Inf Sci Syst

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This study develops a pattern recognition method that identifies patterns based on their similarity and their association with the outcome of interest. The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury. This grouping is based on distinctive patterns in health service use within the first week post-injury. The groups also provide predictive information towards the total cost of medication process. As a result, the group of patients who have undesirable outcomes are identified as early as possible based health service use patterns. Methods:We propose a multi-objective optimization model to group patients. An objective function is the cost function of k-medians clustering to recognize the similar patterns. Another objective function is the cross-validated root-mean-square error to examine the association with the total cost. The best grouping is obtained by minimizing both objective functions. As a result, the multi-objective optimization model is a semi-supervised clustering which learns health service use patterns in both unsupervised and supervised ways. We also introduce an evolutionary computation approach includes stochastic gradient descent and Pareto optimal solutions to find the optimal solution. In addition, we use the decision tree method to reproduce the optimal groups using an interpretable classification model. Results:The results show that the proposed multi-objective semi-supervised clustering identifies distinct groups of health service uses and contributes to predict the total cost. The performance of the multi-objective model has been examined using two metrics such as the average silhouette width and the cross-validation error. The examination proves that the multi-objective model outperforms the single-objective ones. In addition, the interpretable classification model shows that imaging and therapeutic services are critical services in the first-week post-injury to group injured patients. Conclusion:The proposed multi-objective semi-supervised clustering finds the optimal clusters that not only are well-separated from each other but can provide informative insights regarding the outcome of interest. It also overcomes two drawback of clustering methods such as being sensitive to the initial cluster centers and need for specifying the number of clusters.

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“…This improves both safety and explainability of possible Machine Learning (ML) modules used to process the data. Relevant information can be extracted from documents [13] or from video streams [14], also combined with instrument usage signals (e.g. kinematics in case of robotic surgery) [15].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Surgical Procedures Using Statecharts for Semi-Autonomous Robotic Surgery

Falezza

Piccinelli

Rossi

et al. 2021

IEEE Trans. Med. Robot. Bionics

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In this paper we propose a new methodology to model surgical procedures that is specifically tailored to semiautonomous robotic surgery. We propose to use a restricted version of statecharts to merge the bottom-up approach, based on data-driven techniques (e.g. machine learning), with the top-down approach based on knowledge representation techniques. We consider medical knowledge about the procedure and sensing of the environment in two concurrent regions of the statecharts to facilitate re-usability and adaptability of the modules. Our approach allows producing a well defined procedural model exploiting the hierarchy capability of the statecharts, while machine learning modules act as soft sensors to trigger state transitions. Integrating data driven and prior knowledge techniques provides a robust, modular, flexible and re-configurable methodology to define a surgical procedure which is comprehensible by both humans and machines. We validate our approach on the three surgical phases of a Robot-Assisted Radical Prostatectomy (RARP) that directly involve the assistant surgeon: bladder mobilization, bladder neck transection, and vesicourethral anastomosis, all performed on synthetic manikins.

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Finding discriminative and interpretable patterns in sequences of surgical activities

Cited by 8 publications

References 26 publications

Current Applications of Machine Learning in Spine: From Clinical View

Current Applications of Machine Learning in Spine: From Clinical View

Multi-objective semi-supervised clustering to identify health service patterns for injured patients

Modeling of Surgical Procedures Using Statecharts for Semi-Autonomous Robotic Surgery

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