- •Foreword
- •Preface
- •Contents
- •1.1 Introduction
- •1.2 Method
- •1.2.1 Databases
- •1.2.2 Dates
- •1.2.3 Keywords
- •1.2.4 Criteria for Inclusion
- •1.2.5 Criteria for Exclusion
- •1.2.6 Selection of Papers
- •1.3 Results
- •1.3.1 Subspecialty
- •1.3.2 Type of Telemedicine
- •1.3.3 Study Design
- •1.3.4 Final Conclusions of Papers
- •1.4 Discussion
- •References
- •2.1 Introduction
- •2.2 The Need for Diabetic Retinopathy Screening Programs
- •2.4 Guidelines for Referring Patients
- •2.7 Program Models for Diabetic Retinopathy Screening
- •2.9 Program Personnel and Operations
- •2.9.1 Primary Care Providers
- •2.9.2 Photographers
- •2.9.3 Clinical Consultants
- •2.9.4 Administrators
- •2.9.5 A Note to CEOs, Operations Directors, and Clinic Managers
- •2.10 Policies and Procedures
- •2.10.1 Sample Protocol 1
- •2.10.1.1 Diabetic Retinopathy Screening Services
- •Policy
- •Background
- •Procedure
- •2.10.2 Sample Protocol 2
- •2.10.2.1 Pupil Dilation Before Diabetic Retinopathy Photography
- •Policy
- •Background
- •Procedure
- •2.10.3 Sample Protocol 3
- •2.10.3.1 Diabetic Retinopathy Photography Review
- •Policy
- •Background
- •Procedure
- •2.11 Technical Requirements
- •2.11.1 Connectivity
- •2.11.2 Resolution
- •2.11.3 Color
- •2.11.4 Stereopsis
- •2.11.5 Compression
- •2.11.6 Enhancement
- •2.11.7 Pupil Dilation
- •2.11.8 Early California Telemedicine Initiatives Diabetic Retinopathy Screening
- •2.11.9 The American Indian Diabetes Teleophthalmology Grant Program
- •2.11.10 Central Valley EyePACS Diabetic Retinopathy Screening Project
- •2.12.1 Diabetic Retinopathy
- •2.12.1.1 ADA Guidelines Terms
- •2.12.1.2 Vitrectomy
- •References
- •3: Stereopsis and Teleophthalmology
- •3.1 Introduction
- •3.2 History of Stereopsis and Stereopsis in Ophthalmology
- •3.3 Technology and Photography
- •3.3.3 Imaging Fields
- •3.3.4 Image Viewing Techniques
- •3.3.5 Image Compression
- •3.4 Stereoscopic Teleophthalmology Systems
- •3.4.1 University of Alberta
- •3.4.4 Joslin Vision Network
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Methods
- •4.2.1 Main Outcome Measures
- •4.3 Results
- •4.3.1 Retinal Video Recording Versus Retinal Still Photography
- •4.3.2 Video Compression Analysis
- •4.4 Discussion
- •References
- •5.1 Introduction
- •5.1.1 Automated, Remote Image Analysis of Retinal Diseases
- •5.1.2 Telehealth
- •5.2 Design Requirements
- •5.2.1 Telehealth Network Architecture
- •5.2.2 Work Flow
- •5.2.3 Performance Evaluation of the Network
- •5.3 Automated Image Analysis Overview
- •5.3.1 Quality Assessment Module
- •5.3.2 Vascular Tree Segmentation
- •5.3.3 Quality Evaluation
- •5.4 Anatomic Structure Segmentation
- •5.4.1 Optic Nerve Detection
- •5.4.2 Macula
- •5.4.3 Lesion Segmentation
- •5.4.4 Lesion Population Description
- •5.4.5 Image Query
- •5.5 Summary
- •References
- •6.1 Introduction
- •6.3 Optical Coherence Tomography to Detect Leakage
- •References
- •7.1 Introduction
- •7.2 Patients and Methods
- •7.2.1 Participants
- •7.2.2 Methods
- •7.2.3 Statistics
- •7.3 Results
- •7.3.1 Reliability of Image Evaluation
- •7.3.2 Prevalence of Glaucomatous Optic Nerve Atrophy
- •7.4 Discussion
- •7.5 Perspectives
- •References
- •8.1 Introduction
- •8.1.2 Homology Between Retinal and Systemic Microvasculature
- •8.1.3 Need for More Precise CVD Risk Prediction
- •8.2.1 Retinal Microvascular Signs
- •8.2.2 Retinal Vessel Biometry
- •8.2.3 Newer Retinal Imaging for Morphologic Features of Retinal Vasculature
- •8.3 Associations of Retinal Imaging and CVD Risk
- •8.3.1.1 Risk of Pre-clinical CVD
- •8.3.1.2 Risk of Stroke
- •8.3.1.3 Risk of Coronary Heart Disease
- •8.3.2.1 Risk of Hypertension
- •8.3.2.2 Risk of Stroke
- •8.3.2.3 Risk of Coronary Heart Disease
- •8.3.2.4 Risk of Peripheral Artery Disease
- •8.3.3 Newer Morphologic Features of Retinal Vasculature
- •8.4 Retinal Imaging and Its Potential as a Tool for CVD Risk Prediction
- •References
- •9.1 Alzheimer’s Disease
- •9.2 Treatments
- •9.3 Diagnosis
- •9.6 Conclusions
- •References
- •10.1 Introduction
- •10.1.1 Stroke
- •10.1.2 Heart Disease
- •10.1.3 Arteriovenous Ratio
- •10.2 Purpose
- •10.3 Method
- •10.3.1 Medical Approach
- •10.3.2 Technical Approach
- •10.3.3 Output of Medical Data
- •10.4 Patients
- •10.5 Results
- •10.5.1 Medical History
- •10.5.2 Telemedical Evaluation of Retinal Vessels
- •10.5.2.1 Prevalence of Retinal Microangiopathy
- •10.5.2.2 Arteriovenous Ratio
- •10.5.2.3 PROCAM-Index
- •10.6 Discussion and Perceptive
- •10.6.1 Estimation of “Stroke Risk” Estimated by the Stage of Retinal Microangiopathy
- •References
- •11.1 Introduction
- •11.2 System Requirements
- •11.3 Fundus Camera
- •11.4 Imaging Procedure
- •11.4.1 Reading Center Procedure
- •11.5 Detection of Macular Edema
- •11.6 Implementation
- •11.7 Unreadable Images
- •11.7.1 Impact on Overall Diabetic Retinopathy Assessment Rates
- •11.7.2 Compliance with Recommendations
- •11.7.3 Challenges
- •11.7.4 Summary
- •References
- •12.1 Screening
- •12.2 Background
- •12.3 Historical Perspective in England
- •12.4 Methodology
- •12.4.1 The Aim of the Programme
- •12.5 Systematic DR Screening
- •12.6 Cameras for Use in the English Screening Programme
- •12.7 Software for Use in the English Screening Programme
- •12.9 Implementation in England
- •12.11 Quality Assurance
- •12.12 The Development of External Quality Assurance in the English Screening Programme
- •12.13 Information Technology (IT) Developments for the English Screening Programme
- •12.14 Dataset Development
- •12.15 The Development of External Quality Assurance Test Set for the English Screening Programme
- •12.16 Failsafe
- •12.17 The Epidemic of Diabetes
- •References
- •13.1 Introduction
- •13.2 Burden of Diabetes and Diabetic Retinopathy in India
- •13.3 Diabetic Retinopathy Screening Models
- •13.4 Need for Telescreening
- •13.5 Guidelines for Telescreening
- •13.6 ATA Categories of DR Telescreening Validation
- •13.7 Yield of Diabetic Retinopathy in a Telescreening Model
- •13.8 How Are Images Transferred
- •13.10 How Many Fields Are Enough for Diabetic Retinopathy Screening
- •13.11 Is Mydriasis Needed While Using Nonmydriatic Camera?
- •13.12 Validation Studies on Telescreening
- •13.12.1 Accuracy of Telescreening
- •13.12.2 Patient Satisfaction in Telescreening
- •13.12.3 Cost Effectivity
- •13.12.4 Telescreening for Diabetic Retinopathy: Our Experience
- •13.13 Future of Diabetic Retinopathy Screening
- •References
- •14.1 Introduction
- •14.2 Methods
- •14.3 Discussion
- •14.4 Conclusion
- •References
- •15.1 Introduction
- •15.1.1 Description of the EADRSI
- •15.5 State Support of Screening in the Safety Net
- •15.7 Screening Economics for Providers
- •15.8 Patient Sensitivity to Fees
- •15.9 Conclusion
- •References
- •16.1 Introduction
- •16.2 Setting Up the New Screening Model
- •16.2.1 Phase 1: Training
- •16.2.2 Phase 2: Evaluation of Agreement
- •16.2.3 Phase 3: Implementation of the Screening Model
- •16.3 Technologic Requirements
- •16.3.1 Data Management
- •16.3.2 Data Models
- •16.3.2.1 Data Scheme for Patient-Related Information
- •16.3.2.2 Data Scheme for Images
- •Fundus Camera VISUCAM Pro NM
- •PACS Server
- •ClearCanvas DICOM Visualizer
- •16.4 Results
- •16.4.1 Phase 2: Agreement Evaluation
- •16.4.2 Phase 3: Implementation of the Screening Model
- •16.5 Discussion
- •16.5.1 Evaluation of the Screening Model
- •16.5.2 Prevalence of DR
- •16.5.3 Quality Evaluation
- •16.6 Conclusion
- •References
- •17.1.3 Examination and Treatment
- •17.1.4 Limitations of Current Care
- •17.2 Telemedicine and ROP
- •17.2.2 Accuracy and Reliability of Telemedicine for ROP Diagnosis
- •17.2.3 Operational ROP Telemedicine Systems
- •17.2.4 Potential Barriers
- •17.3 Closing Remarks
- •17.3.1 Future Directions
- •References
- •18.1 Introduction
- •18.2 Neonatal Stress and Pain
- •18.3 ROP Screening Technique
- •18.4 Effect of Different Examination Techniques on Stress
- •18.5 Future of Retinal Imaging in Babies
- •References
- •19.1 Introduction
- •19.2 History of the Program
- •19.3 Telehealth Technologies
- •19.4 Impact of the Program
- •Selected References
- •Preamble
- •Introduction
- •Background
- •The Diabetic Retinopathy Study (DRS)
- •Mission
- •Vision
- •Goals
- •Guiding Principles
- •Ethics
- •Clinical Validation
- •Category 1
- •Category 2
- •Category 3
- •Category 4
- •Communication
- •Medical Care Supervision
- •Patient Care Coordinator
- •Image Acquisition
- •Image Review and Evaluation
- •Information Systems
- •Interoperability
- •Image Acquisition
- •Compression
- •Data Communication and Transmission
- •Computer Display
- •Archiving and Retrieval
- •Security
- •Reliability and Redundancy
- •Documentation
- •Image Analysis
- •Legal Requirements
- •Facility Accreditation
- •Privileging and Credentialing
- •Stark Act and Self-referrals
- •State Medical Practice Acts/Licensure
- •Tort Liability
- •Duty
- •Standards of Care
- •Consent
- •Quality Control
- •Operations
- •Customer Support
- •Originating Site
- •Transmission
- •Distant Site
- •Financial Factors
- •Reimbursement
- •Grants
- •Federal Programs
- •Other Financial Factors
- •Equipment Cost
- •Summary
- •Abbreviations
- •Appendices
- •Appendix A: Interoperability
- •Appendix B: DICOM Metadata
- •Appendix C: Computer-Aided Detection
- •Appendix D: Health Insurance Portability and Accountability Act (HIPAA)
- •Appendix F: Quality Control
- •Appendix H: Customer Support
- •Level 1
- •Level 2
- •Level 3
- •Appendix I: Reimbursement
- •Medicare
- •Medicaid
- •Commercial Insurance Carrier Reimbursement
- •Other Financial Factors
- •Disease Prevention
- •Resource Utilization
- •American Telemedicine Association’s Telehealth Practice Recommendations for Diabetic Retinopathy
- •Conclusion
- •References
- •Contributors
- •Second Edition
- •First Edition
- •Index
5 Automated Image Analysis and the Application of Diagnostic Algorithms in an Ocular Telehealth Network 55
5.5Summary
In this chapter, we have described our work using CBIR and retinal image databases in a regional ocular telehealth network. The network permits remote diagnosis of DR using high-throughput methods to meet the growing need for highthroughput disease assessment and management. The network infrastructure for automated diagnosis of DR provides a method for low-cost, realtime diagnosis and patient referral in the primary care environment. We also describe the design of the underlying network infrastructure, which emphasizes high-speed data transmission for real-time image analysis, secure data encryption, and cost-effective implementation and transmission of protected health information to meet Federal HIPAA compliance regulations.
Acknowledgments These studies were supported in part by grants from Oak Ridge National Laboratory, the National Eye Institute, (EY017065), and the Health Resources and Services Administration, by an unrestricted UTHSC departmental grant from Research to Prevent Blindness, New York, NY and by the Plough Foundation, Memphis, TN.
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Computer-Aided Detection |
6 |
of Diabetic Retinopathy Progression |
José Cunha-Vaz, Rui Bernardes, Torcato Santos,
Carlos Oliveira, Conceição Lobo, Isabel Pires,
and Luisa Ribeiro
6.1Introduction
Diabetic retinopathy (DR) is the leading cause of low vision and blindness in people of working age in Europe and United States and the more common microvascular complication of diabetes. It is also projected that during the next 20–30 years, the
J. Cunha-Vaz ( ) • R. Bernardes
AIBILI – Association for Innovation and Biomedical Research on Light and Image, Coimbra, Portugal
Faculty of Medicine, University of Coimbra, Centre of New Technologies for Medicine,
Coimbra, Portugal
e-mail: cunhavaz@aibili.pt
T. Santos • L. Ribeiro
AIBILI – Association for Innovation and Biomedical Research on Light and Image, Centre of New Technologies for Medicine, Coimbra, Portugal
C. Oliveira
Critical Health, Centre for Clinical Trails, Coimbra, Portugal
C. Lobo
AIBILI – Association for Innovation and Biomedical Research on Light and Image, Centre of New Technologies for Medicine, Coimbra, Portugal
Faculty of Medicine, University of Coimbra,
Coimbra, Portugal
Department of Ophthalmology, University Hospital
of Coimbra, Coimbra, Portugal
I. Pires
AIBILI – Association for Innovation and Biomedical Research on Light and Image Centre for Clinical Trails, Coimbra, Portugal
Department of Ophthalmology, University Hospital
of Coimbra, Coimbra, Portugal
number of persons affected with diabetes mellitus will increase by as much as 35% [1]. It is well recognized from clinical experience that the evolution and progression of DR varies between different individuals independently of the duration of the disease and the status of its metabolic control. Diabetic patients with similar levels of chronic hyperglycaemia do not develop necessarily the same DR complications, and not every patient develops macular oedema or proliferative retinopathy, the complications associated with vision loss.
There is now accumulated evidence indicating that only the non-proliferative stage of DR (NPDR) is directly due to the systemic disease and associated hyperglycaemia. Macular oedema and proliferative diabetic retinopathy are late complications of diabetic retinopathy. Macular oedema is a direct result of a widespread alteration of the blood-retinal barrier, and proliferative retinopathy occurs only after the development of large areas of capillary closure with the ensuring ischaemia. Neovascularization in DR is the direct result of the ischaemia and when established is not influenced by the diabetic metabolic control. Its course and management are not different from other situations in the retina where neovascularization develops such as retinal vein occlusion [2].
Diabetic retinal lesions are still reversible at the initial stages of mild NPDR, before the complications of DR, macular oedema and proliferative retinopathy occur. It is this stage of the disease that needs to be well characterized if we want to stop disease progression and improve management of DR.
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Four main alterations characterize the early stages of DR: microaneurysms/haemorrhages, alteration of the blood-retinal barrier, capillary closure and alterations of the neuronal and glial cells of the retina. These alterations may be monitored by microaneurysms counting methodologies and retinal thickness measurements. A combination of these methods using novel methods of multimodal imaging of the retina has contributed to the identification of three different phenotypes of NPDR, showing different patterns of disease progression: phenotype A, including eyes with little abnormal leakage, a slow rate of microaneurysm formation and no signs of capillary closure; phenotype B, including eyes characterized by persistently high leakage values and increased retinal thickness measurements, variable rates of microaneurysm formation and no signs of capillary closure (in this phenotype, the alteration of the blood-retinal barrier is the dominant feature); and phenotype C, including eyes with variable leakage and variable retinal thickness values, high rates of microaneurysm formation and disappearance and clear signs of capillary closure. This third phenotype shows a clear predominance of capillary closure and early development of retinal ischaemia. Long-term follow-up of these different groups of eyes/patients for a period of 7 years showed that only eyes/patients belonging to phenotypes B and C developed clinical significant macular oedema with clear indication for photocoagulation treatment according to ETDRS guidelines. None of the eyes identified initially as phenotype A developed after 7 years of follow-up severe macular oedema needing laser photocoagulation. In summary, the phenotype A is characterized by lack of progression, suggesting that this phenotype has a slow evolution without development of the characteristic complications of NPDR, macular oedema and proliferative retinopathy, at least during a period of 7 years.
On the other hand, the other DR phenotypes, the leaky type or phenotype B and the ischaemic type or phenotype C, lead much more frequently to the development of severe macular oedema with incidences at 7 years of 41% and 50%, respectively [3].
The characterization of these three different phenotypes of NPDR confirms the general clinical impression that the evolution and progression of DR varies between different individuals.
6.2Automated Monitoring
of Retinopathy Progression: Microaneurysm Turnover
It is, therefore, of fundamental importance to monitor the progression of the disease in a specific patient and identify if he is a ‘progressor’, i.e. a patient that shows signs of rapid progression and to which phenotype of progression he belongs. Some eyes/patients need special attention and timely intervention to avoid development of the DR complications, macular oedema or proliferative DR.
The major alterations that occur in NPDR and need to be monitored are microaneurysms dynamics, namely, their formation and disappearance, vascular leakage with subsequent oedema and hard exudates formation and capillary closure.
Visual function loss occurs characteristically late in DR because the eye has a large functional reserve of vision, and DR affects initially the inner layers of the retina away from the photoreceptors. Therefore, structural changes are detected in DR earlier than functional changes. We have, therefore, to focus on evidence of structural changes if we want to follow progression in the earliest stages of DR.
One of the best candidates for non-invasive imaging of the eye fundus is clearly fundus digital photography because retinal cameras are widely available, and the data obtained may be supported and enhanced by computer-assisted procedures.
To identify progression it is essential to collect sequential series of images, and these images must be compared. The need for co-registration of these sequences of images is, therefore, of great relevance. By applying novel co-registration procedures and automated comparative analysis software, it is now possible to perform reliable sequential comparisons of fundus digital photography images.
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Fig. 6.1 This figure illustrates the automatic MA tracking over time, colour coding each detected MA as new, old or disappeared (based on proprietary co-registration algorithm)
Fig. 6.2 The Retmarker software automatically calculates MA formation and disappearance rates. The patient above had a MA formation rate of 5 MA/year over a 24-month follow-up
The RetmarkerDR is a software now available (Critical Health, Portugal) which is able to automatically detect changes occurring in eye fundus digital images, by comparing successive visits to the reference images, in each eye, based on co-registration and co-localization of the changes (Figs. 6.1 and 6.2).
On fundus photography, microaneurysms and small haemorrhages are the initial changes detected in the diabetic retina. They may be
counted, and retinal microaneurysm counting has been suggested as an appropriate marker of retinopathy progression [4, 5].
Retinal microaneurysms are important lesions of diabetic retinopathy, and even one or two microaneurysms in an eye should not be regarded as unimportant [6]. When examining 1,809 patients in the UKPDS cohort that had either no retinopathy or microaneurysms only at entry, they showed that the number of microaneurysms
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had a high predictive value for worsening retinopathy at 3, 6, 9 and 12 years after entry into the study [6]. Similar findings had been presented by Klein et al. who looked at the relationship of retinal microaneurysms to the progression of diabetic retinopathy over a 4-year period [7]. In their study, the number of microaneurysms at the baseline examination was positively associated with significant progression of retinopathy.
More recently, Sjolie et al. showed that microaneurysms counts were predictive of an increased risk of retinopathy [8].
However, our research has shown that the total number of microaneurysms detected in colour fundus photographs offers lower sensitivity in detecting progression of the retinopathy when compared with the determination of microaneurysms turnover, taking into account the exact location of new microaneurysms in successive fundus photographs taken at 6-month or 1-year intervals, apparently because the regressed microaneurysms are constantly balanced by the new ones [9].
We found that differences between successive visits using microaneurysm counts are less reliable than microaneurysm formation rates, which take into account newly formed microaneurysms and give more accurate information on ‘activity of the retinopathy’. Furthermore, we have also found much better agreement between graders when determining microaneurysm turnover [10].
Recently, Sharp et al. [11] found that the microaneurysm turnover varied widely between eyes of the same retinopathy level. This is also consistent with our findings. Microaneurysm turnover has been shown in our studies to vary between eyes that were classified with the same retinopathy level. Particularly relevant and of major interest is the finding that the patients who have higher microaneurysm turnover values are the ones that go on to develop clinically significant macular oedema (CSME) and show a more rapid retinopathy progression, particularly in association with poor metabolic control demonstrated by higher HbA1c values. Microaneurysm turnover appears to be a distinctive characteristic that indicates activity of disease and rapid progression in eyes with apparently similar retinopathy level.
The observation that in the group with diabetes type 2, the level of metabolic control, given by HbA1c values, correlates with retinopathy progression confirms previous reports [12]. It is interesting that other systemic variables, such as blood pressure and blood-lipid levels, did not appear to be relevant in this relatively wellcontrolled group of patients.
Microaneurysms are the key lesion in the early stages of DR, and our work demonstrates consistency in the demonstration of microaneurysm turnover values [10]. Our studies demonstrate that it is not the absolute total number of microaneurysms at a certain point in time that may provide the best indication of retinopathy progression, but the rate of microaneurysm turnover in successive visits during a 1- or 2-year period.
We have shown that it is possible to use microaneurysm computed from non-invasive colour fundus photographs as a biomarker to identify eye/patients at risk of progression for CSME. A microaneurysm formation rate of at least two microaneurysms/eye in eyes with mild NPDR and diabetes type 2 appears to identify patients at risk for progression to CSME as well. In one recent and larger study, with a 10-year follow-up of 113 eyes/patients, the percentage of false negatives (eyes that developed CSME with a low microaneurysm formation rate) was 29.4% (5/17), and the percentage of false positives (eyes that did not develop CSME with a high microaneurysm formation rate) was 8.3% (8/96), resulting in a sensitivity in predicting CSME development of 70.6%. The high negative predictive value for CSME (94.6%, 88/93) indicates that a low microaneurysm turnover, i.e. less than two microaneurysms/ year, identifies particularly well the eyes/patients that are not expected to progress to CSME within a 10-year period.
In another study, a group of 290 eyes that were followed by fundus photography during a period of 5 years in the Caldiret study, coordinated by Munich University, it was possible to compare 49 eyes that did develop CSME over the period of the study with 241 eyes that did not develop CSME in the same 5-year period (Ulbig, M., et al., personal communication 2011).
