Focal Spot, Spring 1989

https://digitalcommons.wustl.edu/focal_spot_archives/1051/thumbnail.jp

Interfaces for Modular Surgical Planning and Assistance Systems

Modern surgery of the 21st century relies in many aspects on computers or, in a wider sense, digital data processing. Department administration, OR scheduling, billing, and - with increasing pervasion - patient data management are performed with the aid of so called Surgical Information Systems (SIS) or, more general, Hospital Information Systems (HIS). Computer Assisted Surgery (CAS) summarizes techniques which assist a surgeon in the preparation and conduction of surgical interventions. Today still predominantly based on radiology images, these techniques include the preoperative determination of an optimal surgical strategy and intraoperative systems which aim at increasing the accuracy of surgical manipulations. CAS is a relatively young field of computer science. One of the unsolved "teething troubles" of CAS is the absence of technical standards for the interconnectivity of CAS system. Current CAS systems are usually "islands of information" with no connection to other devices within the operating room or hospital-wide information systems. Several workshop reports and individual publications point out that this situation leads to ergonomic, logistic, and economic limitations in hospital work. Perioperative processes are prolonged by the manual installation and configuration of an increasing amount of technical devices. Intraoperatively, a large amount of the surgeons'' attention is absorbed by the requirement to monitor and operate systems. The need for open infrastructures which enable the integration of CAS devices from different vendors in order to exchange information as well as commands among these devices through a network has been identified by numerous experts with backgrounds in medicine as well as engineering. This thesis contains two approaches to the integration of CAS systems: - For perioperative data exchange, the specification of new data structures as an amendment to the existing DICOM standard for radiology image management is presented. The extension of DICOM towards surgical application allows for the seamless integration of surgical planning and reporting systems into DICOM-based Picture Archiving and Communication Systems (PACS) as they are installed in most hospitals for the exchange and long-term archival of patient images and image-related patient data. - For the integration of intraoperatively used CAS devices, such as, e.g., navigation systems, video image sources, or biosensors, the concept of a surgical middleware is presented. A c++ class library, the TiCoLi, is presented which facilitates the configuration of ad-hoc networks among the modules of a distributed CAS system as well as the exchange of data streams, singular data objects, and commands between these modules. The TiCoLi is the first software library for a surgical field of application to implement all of these services. To demonstrate the suitability of the presented specifications and their implementation, two modular CAS applications are presented which utilize the proposed DICOM extensions for perioperative exchange of surgical planning data as well as the TiCoLi for establishing an intraoperative network of autonomous, yet not independent, CAS modules.Die moderne Hochleistungschirurgie des 21. Jahrhunderts ist auf vielerlei Weise abhängig von Computern oder, im weiteren Sinne, der digitalen Datenverarbeitung. Administrative Abläufe, wie die Erstellung von Nutzungsplänen für die verfügbaren technischen, räumlichen und personellen Ressourcen, die Rechnungsstellung und - in zunehmendem Maße - die Verwaltung und Archivierung von Patientendaten werden mit Hilfe von digitalen Informationssystemen rationell und effizient durchgeführt. Innerhalb der Krankenhausinformationssysteme (KIS, oder englisch HIS) stehen für die speziellen Bedürfnisse der einzelnen Fachabteilungen oft spezifische Informationssysteme zur Verfügung. Chirurgieinformationssysteme (CIS, oder englisch SIS) decken hierbei vor allen Dingen die Bereiche Operationsplanung sowie Materialwirtschaft für spezifisch chirurgische Verbrauchsmaterialien ab. Während die genannten HIS und SIS vornehmlich der Optimierung administrativer Aufgaben dienen, stehen die Systeme der Computerassistierten Chirugie (CAS) wesentlich direkter im Dienste der eigentlichen chirugischen Behandlungsplanung und Therapie. Die CAS verwendet Methoden der Robotik, digitalen Bild- und Signalverarbeitung, künstlichen Intelligenz, numerischen Simulation, um nur einige zu nennen, zur patientenspezifischen Behandlungsplanung und zur intraoperativen Unterstützung des OP-Teams, allen voran des Chirurgen. Vor allen Dingen Fortschritte in der räumlichen Verfolgung von Werkzeugen und Patienten ("Tracking"), die Verfügbarkeit dreidimensionaler radiologischer Aufnahmen (CT, MRT, ...) und der Einsatz verschiedener Robotersysteme haben in den vergangenen Jahrzehnten den Einzug des Computers in den Operationssaal - medienwirksam - ermöglicht. Weniger prominent, jedoch keinesfalls von untergeordnetem praktischen Nutzen, sind Beispiele zur automatisierten Überwachung klinischer Messwerte, wie etwa Blutdruck oder Sauerstoffsättigung. Im Gegensatz zu den meist hochgradig verteilten und gut miteinander verwobenen Informationssystemen für die Krankenhausadministration und Patientendatenverwaltung, sind die Systeme der CAS heutzutage meist wenig oder überhaupt nicht miteinander und mit Hintergrundsdatenspeichern vernetzt. Eine Reihe wissenschaftlicher Publikationen und interdisziplinärer Workshops hat sich in den vergangen ein bis zwei Jahrzehnten mit den Problemen des Alltagseinsatzes von CAS Systemen befasst. Mit steigender Intensität wurde hierbei auf den Mangel an infrastrukturiellen Grundlagen für die Vernetzung intraoperativ eingesetzter CAS Systeme miteinander und mit den perioperativ eingesetzten Planungs-, Dokumentations- und Archivierungssystemen hingewiesen. Die sich daraus ergebenden negativen Einflüsse auf die Effizienz perioperativer Abläufe - jedes Gerät muss manuell in Betrieb genommen und mit den spezifischen Daten des nächsten Patienten gefüttert werden - sowie die zunehmende Aufmerksamkeit, welche der Operateur und sein Team auf die Überwachung und dem Betrieb der einzelnen Geräte verwenden muss, werden als eine der "Kinderkrankheiten" dieser relativ jungen Technologie betrachtet und stehen einer Verbreitung über die Grenzen einer engagierten technophilen Nutzergruppe hinaus im Wege. Die vorliegende Arbeit zeigt zwei parallel von einander (jedoch, im Sinne der Schnittstellenkompatibilität, nicht gänzlich unabhängig voneinander) zu betreibende Ansätze zur Integration von CAS Systemen. - Für den perioperativen Datenaustausch wird die Spezifikation zusätzlicher Datenstrukturen zum Transfer chirurgischer Planungsdaten im Rahmen des in radiologischen Bildverarbeitungssystemen weit verbreiteten DICOM Standards vorgeschlagen und an zwei Beispielen vorgeführt. Die Erweiterung des DICOM Standards für den perioperativen Einsatz ermöglicht hierbei die nahtlose Integration chirurgischer Planungssysteme in existierende "Picture Archiving and Communication Systems" (PACS), welche in den meisten Fällen auf dem DICOM Standard basieren oder zumindest damit kompatibel sind. Dadurch ist einerseits der Tatsache Rechnung getragen, dass die patientenspezifische OP-Planung in hohem Masse auf radiologischen Bildern basiert und andererseits sicher gestellt, dass die Planungsergebnisse entsprechend der geltenden Bestimmungen langfristig archiviert und gegen unbefugten Zugriff geschützt sind - PACS Server liefern hier bereits wohlerprobte Lösungen. - Für die integration intraoperativer CAS Systeme, wie etwa Navigationssysteme, Videobildquellen oder Sensoren zur Überwachung der Vitalparameter, wird das Konzept einer "chirurgischen Middleware" vorgestellt. Unter dem Namen TiCoLi wurde eine c++ Klassenbibliothek entwickelt, auf deren Grundlage die Konfiguration von ad-hoc Netzwerken während der OP-Vorbereitung mittels plug-and-play Mechanismen erleichtert wird. Nach erfolgter Konfiguration ermöglicht die TiCoLi den Austausch kontinuierlicher Datenströme sowie einzelner Datenpakete und Kommandos zwischen den Modulen einer verteilten CAS Anwendung durch ein Ethernet-basiertes Netzwerk. Die TiCoLi ist die erste frei verfügbare Klassenbibliothek welche diese Funktionalitäten dediziert für einen Einsatz im chirurgischen Umfeld vereinigt. Zum Nachweis der Tauglichkeit der gezeigten Spezifikationen und deren Implementierungen, werden zwei modulare CAS Anwendungen präsentiert, welche die vorgeschlagenen DICOM Erweiterungen zum perioperativen Austausch von Planungsergebnissen sowie die TiCoLi zum intraoperativen Datenaustausch von Messdaten unter echzeitnahen Anforderungen verwenden

Computer assisted navigation in spine surgery

INTRODUCTION: Computer aided navigation is an important tool which has the capability to enhance surgical accuracy, while reducing negative outcomes. However, it is a relatively new technology and has not yet been accepted as the standard of care in all settings. OBJECTIVES: The objective of the present study is to present the development and current state of technologies in computer aided navigation in Orthopedic Spine Surgery, specifically in navigated placement of pedicle screws, to examine the clinical need for navigation, it's effect on surgical accuracy and clinical outcome and to determine whether the benefits justify the costs, and make recommendations for future use and enhancements. CONCLUSION: Computer aided navigation in pedicle screw placement enhances accuracy, reduces the probability of negative outcomes, reduces the exposure of the patient and staff to radiation, reduces operative time, and provides cost-savings. Future investigations may potentially enhance this effect further with the use of innovative augmented reality type displays

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

Dual-camera infrared guidance for computed tomography biopsy procedures

A CT-guided biopsy is a specialised surgical procedure whereby a needle is used to withdraw tissue or fluid specimen from a lesion of interest. The needle is guided while being viewed by a clinician on a computed tomography (CT) scan. CT guided biopsies invariably expose patients and operators to high dosage of radiation and are lengthy procedures where the lack of spatial referencing while guiding the needle along the required entry path are some of the diffculties currently encountered. This research focuses on addressing two of the challenges clinicians currently face when performing CT-guided biopsy procedures. The first challenge is the lack of spatial referencing during a biopsy procedure, with the requirement for improved accuracy and reduction in the number of repeated scans. In order to achieve this an infrared navigation system was designed and implemented where an existing approach was subsequently extended to help guide the clinician in advancing the biopsy needle. This extended algorithm computed a scaled estimate of the needle endpoint and assists with navigating the biopsy needle through a dedicated and custom built graphical user interface. The second challenge was to design and implement a training environment where clinicians could practice different entry angles and scenarios. A prototype training module was designed and built to provide simulated biopsy procedures in order to help increase spatial referencing. Various experiments and different scenarios were designed and tested to demonstrate the correctness of the algorithm and provide real-life simulated scenarios where the operators had a chance to practice different entry angles and familiarise themselves with the equipment. A comprehensive survey was also undertaken to investigate the advantages and disadvantages of the system

Computer Assisted Learning in Obstetric Ultrasound

Ultrasound is a dynamic, real-time imaging modality that is widely used in clinical obstetrics. Simulation has been proposed as a training method, but how learners performance translates from the simulator to the clinic is poorly understood. Widely accepted, validated and objective measures of ultrasound competency have not been established for clinical practice. These are important because previous works have noted that some individuals do not achieve expert-like performance despite daily usage of obstetric ultrasound. Underlying foundation training in ultrasound was thought to be sub-optimal in these cases. Given the widespread use of ultrasound and the importance of accurately estimating the fetal weight for the management of high-risk pregnancies and the potential morbidity associated with iatrogenic prematurity or unrecognised growth restriction, reproducible skill minimising variability is of great importance. In this thesis, I will investigate two methods with the aim of improving training in obstetric ultrasound. The initial work will focus on quantifying operational performance. I collect data in the simulated and clinical environment to compare operator performance between novice and expert performance. In the later work I developed a mixed reality trainer to enhance trainee’s visualisation of how the ultrasound beam interacts with the anatomy being scanned. Mixed reality devices offer potential for trainees because they combine real-world items with items in the virtual world. In the training environment this allows for instructions, 3-dimensional visualisations or workflow instructions to be overlaid on physical models. The work is important because the techniques developed for the qualification of operator skill could be combined in future work with a training programme designed around educational theory to give trainee sonographers consistent feedback and instruction throughout their training

Applications of Artificial Intelligence in Healthcare

Now in these days, artificial intelligence (AI) is playing a major role in healthcare. It has many applications in diagnosis, robotic surgeries, and research, powered by the growing availability of healthcare facts and brisk improvement of analytical techniques. AI is launched in such a way that it has similar knowledge as a human but is more efficient. A robot has the same expertise as a surgeon; even if it takes a longer time for surgery, its sutures, precision, and uniformity are far better than the surgeon, leading to fewer chances of failure. To make all these things possible, AI needs some sets of algorithms. In Artificial Intelligence, there are two key categories: machine learning (ML) and natural language processing (NPL), both of which are necessary to achieve practically any aim in healthcare. The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research. Discoveries and advancements will continue to push the AI frontier and expand the scope of its applications, with rapid developments expected in the future

Introduction to Medical Imaging Informatics

Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.Comment: 17 pages, 11 figures, 2 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging