Cognitive tools pipeline for assistance of mitral valve surgery

Abstract

For cardiac surgeons, mitral valve reconstruction (MVR) surgery is a highly demanding procedure, where an artificial annuloplasty ring is implanted onto the mitral valve annulus to re-enable the valve's proper closing functionality. For a successful operation the surgeon has to keep track of a variety of relevant impact factors, such as patient-individual medical history records, valve geometries, or tissue properties of the surgical target, and thereon-based deduce type and size of the bestsuitable ring prosthesis according to practical surgery experience. With this work, we aim at supporting the surgeon in selecting this ring prosthesis by means of a comprehensive information processing pipeline. It gathers all available patient-individual information, and mines this data according to 'surgical rules', that represent published MVR expert knowledge and recommended best practices, in order to suggest a set of potentially suitable annuloplasty rings. Subsequently, these rings are employed in biomechanical MVR simulation scenarios, which simulate the behavior of the patient-specific mitral valve subjected to the respective virtual ring implantation. We present the implementation of our deductive system for MVR ring selection and how it is integrated into a cognitive data processing pipeline architecture, which is built under consideration of Linked Data principles in order to facilitate holistic information processing of heterogeneous medical data. By the example of MVR surgery, we demonstrate the ease of use and the applicability of our development. We expect to essentially support patient-specific decision making in MVR surgery by means of this holistic information processing approach.

A Review of Cognitive Support Systems in the Operating Room

“…Medical imaging databases such as XNAT are developed for imaging upload and information extraction. 14 For all the papers in this review, an overwhelming 23 (58%) CSTs used X-ray-based images such as computed tomography (CT) as their primary data source. 6 (15%) CSTs adopted MRI as their data source while another 6(15%) CSTs used ultrasound; however, they are not as common as the CT scans due to a variety of different reasons relating to costs and accuracy.…”

“… 20 , 21 Interestingly, contradictive reports were observed between 2 studies; Dixon reported a decrease in recognition while Marcus reported an increase in recognition. 20 , 22 Additionally, 2 studies involving a surgical predictive tool reported numerical values assessing the efficacy of their algorithm 14 , 23 ( Table 4 ).…”

“…For example, in mitral valve replacement surgery, an algorithm was created to reduce commercial mitral valve options based on patient-specific heart dimensions and medical history. 14 In neurosurgery planning, a CST was developed to create a dynamic 3D model of a patient's brain and pathology, providing recommendations for surgical procedures. 22 , 36 CSTs should progress from cognitive support for individual surgeons to support for the entire surgical team, utilizing virtual and augmented reality to improve communication and decision-making efficiency.…”

Surgical data science: the new knowledge domain

“…Technology to automate performance of some surgical activities under supervision is in its nascent stages, with the current work focused on automation of simple tasks such as suturing and knot-tying [59], [60]. Enhanced technologies that either guide or inform surgeons include augmented visualization to guide the operating surgeon or robot-assisted navigation [61], [62], [63], [64], process or workflow analyses for resource management [65], context-aware medical devices [66], surgical trajectory planning [67], automated control of environment in the operating room [68], simulation of SPMs [55], and other virtual reality and cognitive tools for surgical planning [69], [70]. Data analytics that enable technology for such context-aware assistance to surgical care providers are reviewed in detail elsewhere [71].…”