- Email: [email protected]
Research and development challenges of CARS for industry
International Congress Series 1281 (2005) 22 – 27
www.ics-elsevier.com
Research and development challenges of CARS for industry Erich R. Reinhardt Siemens Medical Solutions, Erlangen, Germany
Abstract. As the amount of information available to a physician has increased, the complexity of physician tasks has increased. The information management challenge faced by the clinicians translates to an opportunity for the industry to build solutions, not just products, that help improve the overall quality of clinical care processes, while reducing their costs. These apparently conflicting goals can be achieved only by viewing healthcare as a whole from the objective of benefiting the ultimate customer, the patient. The process of building clinically relevant solutions requires a clear understanding of clinical tasks (problem definition), and developing and deploying solutions such that they will have a quantifiable impact on patient care. In this paper, I will present a framework for development of clinically useful solutions, and present R&D challenges and new developments in selected areas of computer-assisted radiology and surgery. D 2005 CARS & Elsevier B.V. All rights reserved. Keywords: R&D challenges; Clinical workflow; Computer-aided diagnosis; Computer-assisted surgery; Computer-aided radiation therapy; Healthcare IT; Interventional radiology
1. Introduction As the amount of information available to a physician has increased, the complexity of physician tasks has increased. The information management challenge faced by the clinicians translates to an opportunity for the industry to build solutions, not just products, which help improve the overall quality of clinical care processes, while reducing their costs. These apparently conflicting goals can be achieved only by viewing healthcare as a whole from the objective of benefiting the ultimate customer, the patient. The process of building clinically relevant solutions requires a clear understanding of clinical tasks (problem definition), and developing and deploying solutions such that they will have a quantifiable impact on the efficiency of clinical workflows, and ultimately on patient care. E-mail address: [email protected]. 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2005.03.172
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
23
Clinical workflow is a term that involves four categories: prevention, diagnosis, cure and care. In the case of cure, for example, clinical workflow begins when a person falls ill or is injured and does not end until that person is well again (Fig. 1). All of the steps in between have to be optimized in a way that healthcare services become qualitatively better, less expensive, more comfortable for the patient, and faster. Optimization of clinical workflows must therefore begin with understanding patient needs and expectations, which translate into the following clinical needs: 1. accuracy and efficiency in clinical decision making, 2. access to all data relevant to the individual patient, e.g. lab, demographic, images, structured and unstructured doctor’s reports, 3. access to medical knowledge extracted from large patient databases, and finally, 4. ease of use. With the continuous developments in diagnostic and interventional imaging, coupled with new molecular imaging methods, and the impetus to bring all information together in an electronic form (Electronic Patient Record), the potential to build bKnowledge-based ApplicationsQ that impact the continuum of patient care (Fig. 2) is enormous. Just what are these applications? A major challenge is to define or formulate the clinical task to be performed so that technology can be developed and the application can be validated (proven) against the original requirements for improving clinical outcomes. Thus, beginning with a clear understanding of the clinical needs, the key task is to define the problem, followed by development, implementation and deployment of the
Fig. 1. Clinical workflow begins when a person falls ill or is injured and does not end until that person is well again.
24
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
Text extraction
Image analysis
Integrated Medical Database Omics
Data mining
Decision support
Probabilistic inference
Efficient prevention
Text
Images
This is a text note
Workflow
Accurate early diagnosis
Chemistry
In-/Outpatient
Can be structured or unstructured
Lifetime Electronic Patient Record
Personalized therapy
High quality care
Fig. 2. Role of integrated databases and the required technologies to address the decision support needs in the patient care continuum.
solution, in a closed-loop feedback from the validation process to quantify the improvement in clinical outcomes. Outcomes are measured by the bImpact of the product on healthcare quality and the cost of providing the care.Q In this paper, I will outline the R&D challenges and/or present examples of new developments in the following areas using the framework presented above: A) B) C) D) E)
Computer-aided Diagnosis Computer-aided Surgery Cardiovascular and Interventional Radiology Computer-aided Radiation Therapy Integrated Healthcare IT Platforms
2. Computer-aided diagnosis There is much interest and a future need to push the state-of-the art in Computer-aided Diagnosis (CAD) along a number of dimensions: from detection of structures in 2D images to 3D volumes; from computer-aided detection to providing diagnostic decision support in the entire workflow (Fig. 2); from single disease in an organ to comprehensive CAD systems for differential diagnosis of multiple diseases; from oncology applications to neuro and cardiovascular diseases; from detection of diseases to risk assessment and prediction; from analyzing a single image/volume to multi-modality studies and/or serial studies; from analyzing static images to analyzing temporal information for change detection and even motion (in cardiac CAD applications); from using labeled databases for offline training to online database mining systems to show similar cases, or comparison with normal cases; and eventually to integrate all available clinical, genomic, and proteomic patient information with image-based decision support (Fig. 3). The future of clinical application of CAD systems is bright and the scope is broader than ever for researching, developing and deploying clinical decision support systems. The fundamental aspect of clinical utility of a CAD system in improving outcomes by
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
y Multi-modality images images w/ w/ follow-up follow-up
Clinical and financial databases
Individual Patients data
Transcribed text
Frequency
Feature Combination
*omics
10000
Inference
Reliable Extraction and Meaningful Inference from Non-structured Data
REMIND Platform
25000 20000 15000
Feature extraction
25
5000 0 0
5000
10000
15000
20000
M/Z
Large database of labeled patient cases
Learned Knowledge
Clinical domain knowledge
Expert Knowledge
Fig. 3. General representation of components of a decision support system. Large labeled databases and expert clinical knowledge are used by machine learning algorithms. The system is prospectively applied at the point of care with all relevant information available to the system.
enhancing accuracy and efficiency in patient care drives its acceptance by the clinicians as well as regulatory bodies and eventually the payers. Systematic multi-disciplinary research and scientific validation methods on high-quality databases are essential to ensure development of robust CAD systems that live up to their promised capabilities in routine clinical use. 3. Computer-assisted surgery Integrated 3D intra-operative imaging and navigation methods are breaking new grounds in computer-assisted surgery. Consider the following case: The patient is a 35year-old man who suffers from lower back and leg pain. A pre-operative MR shows disc bulge at L4–5, spinal stenosis, and associated inflammation. The surgeon performs a posterior lumbar inter-body fusion with the aid of a navigation system and an Iso-C3D, the 3D imaging capable C-arm. This case shows the clinical benefits of the complementary technologies of intraoperative 3D imaging and optical surgical navigation methods. There is no need for a preop CT in Radiology, which is costly and hospitals often are not reimbursed for scans needed only for the purpose of navigation. Rescanning with the Iso-C after placement of screws can give surgeons enough information that they will not order a post-op scan in Radiology and radiation dose to the patient is lower from fluoro-CT than traditional CT. 4. Cardiovascular and interventional radiology With the integration of flat panel detectors into fluoroscopy systems to replace the traditional image intensifiers, the vision of providing CT-like 3D images during interventional procedures has become a reality. It has opened up new capabilities in 3D
26
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
imaging for planning, guidance and control of minimally invasive procedures in interventional radiology, cardiology and electrophysiology. In particular, it is now possible to image soft tissue of internal organs due to the improved dynamic range of these detectors. This opens up new clinical possibilities, for instance, if an internal hemorrhage is induced during an intravascular intervention, the patient stays on the table of the C-arm system, the catheters or other interventional devices can stay in place and the diagnosis of hemorrhage is obtained almost instantly. The detectability of bleedings, tumors, dilated bile ducts and vascular malformations has been confirmed during the early clinical validation of the 3D imaging system. The next steps are to quantify the clinical outcome in terms of reduced procedure times, improved patient safety and related cost savings. Image guided navigation based on 3D data will be the key element to achieve corresponding improvements for applications in vascular interventions, interventional oncology and percutaneous interventions. An important clinical application in cardiac electrophysiology (EP) is the therapy of abnormal electrical excitations of the heart muscle, like atrial fibrillation, flutter or ventricular tachycardia. The endpoint in EP is to interrupt those abnormal electrical pathways by local application of radiofrequency (RF)-heat or cryo-energy to selected locations. Thereby the delivery of the heat/cryo energy is achieved via catheters which are inserted from the groin and are advanced into the cardiac chambers. The integration of the various technologies is the key to streamline and to optimize the workflow in the EP-lab. Elements of integration range from software solutions based on DICOM or HL7 standards, e.g. exchange of patient demographic data between the systems, to rather sophisticated interactions between imaging and navigation systems. The expectations with respect to the clinical outcomes are manifold: improved success rates in complicated procedures such as ablation of atrial fibrillation, significantly reduced procedure times and reduced X-ray exposure to the patient as well as to the clinicians. Related to that is increased patient safety and comfort as well as ease for the physician, e.g. by remote navigation. 5. Computer-assisted radiation therapy Technological advances in radiotherapy, most notably the introduction of multileaf collimators and consequently the Intensity Modulated Radiation Therapy or IMRT technique, have increased cancer treatment accuracy considerably over the last two decades. This has allowed radiation treatment philosophy to move away from irradiating large areas of the body with unnecessarily large margins around the tumor towards more precisely targeting the tumor with higher doses and sparing more of the healthy tissue. This, in turn, has led to a need to know the exact location of the tumor at any time during the treatment, which can take 4 to 6 weeks. Hence the use of imaging modalities in radiation therapy has grown beyond pre-treatment tumor diagnosis and treatment planning. Increasingly, we find imaging applications in inroom imaging right before each treatment or in more recent approaches even during treatment. In every case, clinical needs must be well understood to define the problem, with a view to seamlessly integrate the imaging technology into the therapy workflow to obtain the desired impact on clinical outcomes.
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
27
Regardless of which imaging system is used, another challenge is the integration of any imaging system into the treatment workflow. This can range from simply correcting the patient position all the way to adaptation of the treatment plan based on changes of the tumor that are caused by the treatment itself. The design of an optimization loop using all information available, including potentially biological gene-information will be the dominant R&D challenge in radiotherapy for the coming years. 6. Integrated healthcare IT platforms With the convergence of enterprise IT needs, to address enterprise efficiency in the framework presented earlier, a software platform for medical systems and applications, integrating patient-related, physiological, and imaging data across the clinical workflow is required. Furthermore, the platform must be able to integrate information systems, modality imaging systems and PACS in multi-vendor environments (the IHE initiative). syngo, Siemens’ proprietary imaging platform, is such a modular platform for medical application development and deployment, common components for all medical modalities, and a common look-and-feel. Some of the key challenges in imaging networks today relate to managing the large data volumes along the clinical workflow in the healthcare enterprise. The clinical requirement for the availability of information anywhere anytime will continue to provide opportunities to develop novel architectures that continue to improve overall performance and capabilities of clinical workflows to ensure positive impact on healthcare quality, clinical outcomes, and the total cost of ownership.
www.ics-elsevier.com
Research and development challenges of CARS for industry Erich R. Reinhardt Siemens Medical Solutions, Erlangen, Germany
Abstract. As the amount of information available to a physician has increased, the complexity of physician tasks has increased. The information management challenge faced by the clinicians translates to an opportunity for the industry to build solutions, not just products, that help improve the overall quality of clinical care processes, while reducing their costs. These apparently conflicting goals can be achieved only by viewing healthcare as a whole from the objective of benefiting the ultimate customer, the patient. The process of building clinically relevant solutions requires a clear understanding of clinical tasks (problem definition), and developing and deploying solutions such that they will have a quantifiable impact on patient care. In this paper, I will present a framework for development of clinically useful solutions, and present R&D challenges and new developments in selected areas of computer-assisted radiology and surgery. D 2005 CARS & Elsevier B.V. All rights reserved. Keywords: R&D challenges; Clinical workflow; Computer-aided diagnosis; Computer-assisted surgery; Computer-aided radiation therapy; Healthcare IT; Interventional radiology
1. Introduction As the amount of information available to a physician has increased, the complexity of physician tasks has increased. The information management challenge faced by the clinicians translates to an opportunity for the industry to build solutions, not just products, which help improve the overall quality of clinical care processes, while reducing their costs. These apparently conflicting goals can be achieved only by viewing healthcare as a whole from the objective of benefiting the ultimate customer, the patient. The process of building clinically relevant solutions requires a clear understanding of clinical tasks (problem definition), and developing and deploying solutions such that they will have a quantifiable impact on the efficiency of clinical workflows, and ultimately on patient care. E-mail address: [email protected]. 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2005.03.172
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
23
Clinical workflow is a term that involves four categories: prevention, diagnosis, cure and care. In the case of cure, for example, clinical workflow begins when a person falls ill or is injured and does not end until that person is well again (Fig. 1). All of the steps in between have to be optimized in a way that healthcare services become qualitatively better, less expensive, more comfortable for the patient, and faster. Optimization of clinical workflows must therefore begin with understanding patient needs and expectations, which translate into the following clinical needs: 1. accuracy and efficiency in clinical decision making, 2. access to all data relevant to the individual patient, e.g. lab, demographic, images, structured and unstructured doctor’s reports, 3. access to medical knowledge extracted from large patient databases, and finally, 4. ease of use. With the continuous developments in diagnostic and interventional imaging, coupled with new molecular imaging methods, and the impetus to bring all information together in an electronic form (Electronic Patient Record), the potential to build bKnowledge-based ApplicationsQ that impact the continuum of patient care (Fig. 2) is enormous. Just what are these applications? A major challenge is to define or formulate the clinical task to be performed so that technology can be developed and the application can be validated (proven) against the original requirements for improving clinical outcomes. Thus, beginning with a clear understanding of the clinical needs, the key task is to define the problem, followed by development, implementation and deployment of the
Fig. 1. Clinical workflow begins when a person falls ill or is injured and does not end until that person is well again.
24
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
Text extraction
Image analysis
Integrated Medical Database Omics
Data mining
Decision support
Probabilistic inference
Efficient prevention
Text
Images
This is a text note
Workflow
Accurate early diagnosis
Chemistry
In-/Outpatient
Can be structured or unstructured
Lifetime Electronic Patient Record
Personalized therapy
High quality care
Fig. 2. Role of integrated databases and the required technologies to address the decision support needs in the patient care continuum.
solution, in a closed-loop feedback from the validation process to quantify the improvement in clinical outcomes. Outcomes are measured by the bImpact of the product on healthcare quality and the cost of providing the care.Q In this paper, I will outline the R&D challenges and/or present examples of new developments in the following areas using the framework presented above: A) B) C) D) E)
Computer-aided Diagnosis Computer-aided Surgery Cardiovascular and Interventional Radiology Computer-aided Radiation Therapy Integrated Healthcare IT Platforms
2. Computer-aided diagnosis There is much interest and a future need to push the state-of-the art in Computer-aided Diagnosis (CAD) along a number of dimensions: from detection of structures in 2D images to 3D volumes; from computer-aided detection to providing diagnostic decision support in the entire workflow (Fig. 2); from single disease in an organ to comprehensive CAD systems for differential diagnosis of multiple diseases; from oncology applications to neuro and cardiovascular diseases; from detection of diseases to risk assessment and prediction; from analyzing a single image/volume to multi-modality studies and/or serial studies; from analyzing static images to analyzing temporal information for change detection and even motion (in cardiac CAD applications); from using labeled databases for offline training to online database mining systems to show similar cases, or comparison with normal cases; and eventually to integrate all available clinical, genomic, and proteomic patient information with image-based decision support (Fig. 3). The future of clinical application of CAD systems is bright and the scope is broader than ever for researching, developing and deploying clinical decision support systems. The fundamental aspect of clinical utility of a CAD system in improving outcomes by
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
y Multi-modality images images w/ w/ follow-up follow-up
Clinical and financial databases
Individual Patients data
Transcribed text
Frequency
Feature Combination
*omics
10000
Inference
Reliable Extraction and Meaningful Inference from Non-structured Data
REMIND Platform
25000 20000 15000
Feature extraction
25
5000 0 0
5000
10000
15000
20000
M/Z
Large database of labeled patient cases
Learned Knowledge
Clinical domain knowledge
Expert Knowledge
Fig. 3. General representation of components of a decision support system. Large labeled databases and expert clinical knowledge are used by machine learning algorithms. The system is prospectively applied at the point of care with all relevant information available to the system.
enhancing accuracy and efficiency in patient care drives its acceptance by the clinicians as well as regulatory bodies and eventually the payers. Systematic multi-disciplinary research and scientific validation methods on high-quality databases are essential to ensure development of robust CAD systems that live up to their promised capabilities in routine clinical use. 3. Computer-assisted surgery Integrated 3D intra-operative imaging and navigation methods are breaking new grounds in computer-assisted surgery. Consider the following case: The patient is a 35year-old man who suffers from lower back and leg pain. A pre-operative MR shows disc bulge at L4–5, spinal stenosis, and associated inflammation. The surgeon performs a posterior lumbar inter-body fusion with the aid of a navigation system and an Iso-C3D, the 3D imaging capable C-arm. This case shows the clinical benefits of the complementary technologies of intraoperative 3D imaging and optical surgical navigation methods. There is no need for a preop CT in Radiology, which is costly and hospitals often are not reimbursed for scans needed only for the purpose of navigation. Rescanning with the Iso-C after placement of screws can give surgeons enough information that they will not order a post-op scan in Radiology and radiation dose to the patient is lower from fluoro-CT than traditional CT. 4. Cardiovascular and interventional radiology With the integration of flat panel detectors into fluoroscopy systems to replace the traditional image intensifiers, the vision of providing CT-like 3D images during interventional procedures has become a reality. It has opened up new capabilities in 3D
26
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
imaging for planning, guidance and control of minimally invasive procedures in interventional radiology, cardiology and electrophysiology. In particular, it is now possible to image soft tissue of internal organs due to the improved dynamic range of these detectors. This opens up new clinical possibilities, for instance, if an internal hemorrhage is induced during an intravascular intervention, the patient stays on the table of the C-arm system, the catheters or other interventional devices can stay in place and the diagnosis of hemorrhage is obtained almost instantly. The detectability of bleedings, tumors, dilated bile ducts and vascular malformations has been confirmed during the early clinical validation of the 3D imaging system. The next steps are to quantify the clinical outcome in terms of reduced procedure times, improved patient safety and related cost savings. Image guided navigation based on 3D data will be the key element to achieve corresponding improvements for applications in vascular interventions, interventional oncology and percutaneous interventions. An important clinical application in cardiac electrophysiology (EP) is the therapy of abnormal electrical excitations of the heart muscle, like atrial fibrillation, flutter or ventricular tachycardia. The endpoint in EP is to interrupt those abnormal electrical pathways by local application of radiofrequency (RF)-heat or cryo-energy to selected locations. Thereby the delivery of the heat/cryo energy is achieved via catheters which are inserted from the groin and are advanced into the cardiac chambers. The integration of the various technologies is the key to streamline and to optimize the workflow in the EP-lab. Elements of integration range from software solutions based on DICOM or HL7 standards, e.g. exchange of patient demographic data between the systems, to rather sophisticated interactions between imaging and navigation systems. The expectations with respect to the clinical outcomes are manifold: improved success rates in complicated procedures such as ablation of atrial fibrillation, significantly reduced procedure times and reduced X-ray exposure to the patient as well as to the clinicians. Related to that is increased patient safety and comfort as well as ease for the physician, e.g. by remote navigation. 5. Computer-assisted radiation therapy Technological advances in radiotherapy, most notably the introduction of multileaf collimators and consequently the Intensity Modulated Radiation Therapy or IMRT technique, have increased cancer treatment accuracy considerably over the last two decades. This has allowed radiation treatment philosophy to move away from irradiating large areas of the body with unnecessarily large margins around the tumor towards more precisely targeting the tumor with higher doses and sparing more of the healthy tissue. This, in turn, has led to a need to know the exact location of the tumor at any time during the treatment, which can take 4 to 6 weeks. Hence the use of imaging modalities in radiation therapy has grown beyond pre-treatment tumor diagnosis and treatment planning. Increasingly, we find imaging applications in inroom imaging right before each treatment or in more recent approaches even during treatment. In every case, clinical needs must be well understood to define the problem, with a view to seamlessly integrate the imaging technology into the therapy workflow to obtain the desired impact on clinical outcomes.
E.R. Reinhardt / International Congress Series 1281 (2005) 22–27
27
Regardless of which imaging system is used, another challenge is the integration of any imaging system into the treatment workflow. This can range from simply correcting the patient position all the way to adaptation of the treatment plan based on changes of the tumor that are caused by the treatment itself. The design of an optimization loop using all information available, including potentially biological gene-information will be the dominant R&D challenge in radiotherapy for the coming years. 6. Integrated healthcare IT platforms With the convergence of enterprise IT needs, to address enterprise efficiency in the framework presented earlier, a software platform for medical systems and applications, integrating patient-related, physiological, and imaging data across the clinical workflow is required. Furthermore, the platform must be able to integrate information systems, modality imaging systems and PACS in multi-vendor environments (the IHE initiative). syngo, Siemens’ proprietary imaging platform, is such a modular platform for medical application development and deployment, common components for all medical modalities, and a common look-and-feel. Some of the key challenges in imaging networks today relate to managing the large data volumes along the clinical workflow in the healthcare enterprise. The clinical requirement for the availability of information anywhere anytime will continue to provide opportunities to develop novel architectures that continue to improve overall performance and capabilities of clinical workflows to ensure positive impact on healthcare quality, clinical outcomes, and the total cost of ownership.