Validation of Knowledge Acquisition for Surgical Process Models

Neumuth, Thomas; Jannin, Pierre; Strauß, Gero; Meixensberger, Juergen; Burgert, Oliver

doi:10.1197/jamia.m2748

Cited by 96 publications

(78 citation statements)

References 12 publications

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“…An universal and adaptable recording scheme has been introduced [14]. It shows how to decompose a surgical intervention into manual work steps.…”

mentioning

confidence: 99%

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Non-linear temporal scaling of surgical processes

Forestier¹,

Petitjean²,

Riffaud³

et al. 2014

Artificial Intelligence in Medicine

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Objective. Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices. Material and Method.We introduce Non-Linear Temporal Scaling (NLTS): a method to realign a set of surgeries along their intrinsic timeline. Experiments are carried out on a set of lumbar disc neurosurgeries. We assess our method both on a highly standardised phase of the surgery (closure) and on the whole surgery.Results. Experiments show that NLTS makes it possible to consistently derive standards of surgical practice and to understand differences between groups of surgeries. We take the training of * Corresponding author -ENSISA -Universite de Haute Alsace, 12 rue des freres Lumiere, 68093 Mulhouse, France, Tel.:+33 3 8933 6963, Fax.:+33 3 8942 3282Email addresses: germain.forestier@uha.fr (Germain Forestier), francois.petitjean@monash.edu (François Petitjean), laurent.riffaud@chu-rennes.fr (Laurent Riffaud), pierre.jannin@univ-rennes1.fr (Pierre Jannin) Preprint submitted to Artificial Intelligence in MedicineSeptember 16, 2013 surgeons as the common theme for the evaluation of the results and highlight, for example, the main differences between the practices of junior and senior surgeons in the removal of a lumbar disc herniation.Conclusion. NLTS realigns a set of sequences along their intrinsic timeline, which makes it possible to extract standards of surgical practices.Supplementary material. The computer code implementing the proposed methods.

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“…An universal and adaptable recording scheme has been introduced [14]. It shows how to decompose a surgical intervention into manual work steps.…”

mentioning

confidence: 99%

“…Automating the analysis of such a dataset is however very challenging. Surgical Process Modelling (SPM) is the field that aims at unlocking this issue [15,16,17,14,18].…”

mentioning

confidence: 99%

Non-linear temporal scaling of surgical processes

Forestier¹,

Petitjean²,

Riffaud³

et al. 2014

Artificial Intelligence in Medicine

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show abstract

“…As Neumuth et al [11] showed, it is possible to create accurate live recordings of surgeries on a detailed, high granularity level. These results showed that the acquired SPM is accurate and reproducible, where the recorded individual SPMs (iSPM) could be used as a basis for process mining techniques.…”

Section: B Bottom-up Approachmentioning

confidence: 99%

Surgical data science, an emerging field of medicine

Nagy

Rudas

Haidegger

2017

2017 IEEE 30th Neumann Colloquium (NC)

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Abstract-Computer Assisted Surgery (CAS) significantly changed the course of interventional medicine. The development of medical imaging opened up the possibility for accurate, patient specific planning, and advanced imaging techniques provided the ground for the development of real-time navigation systems. The advancement of minimally invasive surgical techniques and tools required increasing manuality from the surgeon, which facilitated the development of tele-robotic manipulation. These systems provide a vast amount of objective inta-operative data, thus many believe that the next step could be big data analysis for creating and evaluating surgical process models. This emerging field of medicine, called Surgical Data Science, has the potential to improve intervetional medicine with objective statistical analysis, and therefore to provide better patient outcomes and a reduction in healthcare costs.

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“…Neumuth et al [2] defined Surgical Process (SP) as a set of one or more linked procedures or activities that realize a surgical objective. Surgical Process Models (SPMs) are simply defined by models of surgical interventions.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, data extraction is performed either from a human sight or from sensor devices. In this context, different methods have been recently used for data acquisition: patient specific procedures description [2][3][4], interview of the surgeons [5], sensor-based methods [6][7][8][9][10][11][12][13][14][15][16], using fixed protocols created by expert surgeons [17], or combination between them [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification

Lalys

Riffaud

Morandi

et al. 2010

Information Processing in Computer-Assisted Interventions

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Abstract. The segmentation of the surgical workflow might be helpful for providing context-sensitive user interfaces, or generating automatic report. Our approach focused on the automatic recognition of surgical phases by microscope image classification. Our workflow, including images features extraction, image database labelisation, Principal Component Analysis (PCA) transformation and 10-fold cross-validation studies was performed on a specific type of neurosurgical intervention, the pituitary surgery. Six phases were defined by an expert for this type of intervention. We thus assessed machine learning algorithms along with the data dimension reduction. We finally kept 40 features from the PCA and found a best correct classification rate of the surgical phases of 82% with the multiclass Support Vector Machine.

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Validation of Knowledge Acquisition for Surgical Process Models

Cited by 96 publications

References 12 publications

Non-linear temporal scaling of surgical processes

Non-linear temporal scaling of surgical processes

Surgical data science, an emerging field of medicine

Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification

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