- •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
11 Diabetic Retinopathy Assessment in the Primary Care Environment |
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Fig. 11.5 Composite image of patient with large drusen with standard color fundus image (top image) and DigiScope® image (lower image)
With DigiScope® technology, the incidence of unreadable images was found to increase rapidly with age, resulting in a higher total rate of referral for older patients. Others have also reported that age is the strongest predictor of unreadable images for persons with diabetes, utilizing either mydriatic or nonmydriatic fundus imaging systems [8]. As expected, small pupil size and media opacity occurred more frequently in the older age group as reasons for unreadable DigiScope® images [12]. However, referral of these patients is not a drawback in a screening program since older individuals are at higher risk for ocular pathology and, ideally, should be seen by an ophthalmologist even if they do not have diabetic retinopathy. Cavallerano and collaborators found that the majority of their patients who were referred due to unreadable images actually had ocular disease which would have resulted in referral if adequate images had been obtained [14].
11.7Unreadable Images
One of the concerns when using fundus photography outside of the traditional eye care arena has been the potential for a large proportion of unreadable images. The relatively low rate of unreadable images with our system indicates that the DigiScope® and its operation by nonophthalmic staff can successfully produce useful fundus images. The percentage of unreadable images with the DigiScope® is similar to that reported by other diabetic retinopathy assessment systems, in particular those utilizing nonophthalmic personnel to capture images [13]. The explanation for poor images is not always clearly delineated, and there may be several causes for each unreadable image. In many cases, the reason for an unreadable image may be inferred from the video image of the pupil included with each DigiScope® fundus image. Reasons for unreadable images include poor patient fixation, poor pupil centering, small pupil size (dilating drops not used or not given ample time to take effect), media opacity, and instrument and operator problems [12]. A specific cause for the unreadable image cannot always be determined. The influence of age on the rate of unreadable images has also been investigated.
11.7.1Impact on Overall Diabetic Retinopathy Assessment Rates
In spite of the plethora of telemedicine diabetic retinopathy programs, research reporting on the overall relative value and impact of telemedicine digital retinal assessment is generally lacking. On a small scale, we have been able to show a significant impact on rates of diabetic retinopathy assessment in individual practice settings. In a retrospective 4-year study, all patients with diabetes in a multispecialty primary care practice were tracked for a documented evaluation for diabetic retinopathy with either remote telemedicine imaging or a comprehensive eye examination [19]. In the first year of the study, which served as a baseline prior to implementation of the DigiScope® system, the practice had 1,257 patients with diabetes, and only 15% (190 patients) had a documented assessment for diabetic retinopathy. The DigiScope® program was initiated in the second year of the study. Documented rates of diabetic retinopathy assessment increased steadily to 51% (698 patients) in year 2 and 71% (994 patients) in year 3. By year 4, when the practice had a total of
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1,709 patients with diabetes, the rate of diabetic retinopathy assessment had increased to 85% (1,449 patients). Of the observed increase in number of evaluations, interestingly, 78% was due to increased examinations by ophthalmologists, and only 22% was attributable to remote telemedicine evaluation. The total number of patients with diabetes and the total number evaluated by an ophthalmologist increased significantly over 4 years, while the number of remote retinal imaging assessments increased initially and then stabilized by year 4. These results certainly suggest that implementation of a remote telemedicine evaluation system in the primary care setting can significantly improve compliance with rates of documented evaluations for diabetic retinopathy.
11.7.2Compliance with Recommendations
Clearly, a diabetic retinopathy surveillance system is effective only if a majority of the patients identified with vision-threatening disease seeks further evaluation by an ophthalmologist for possible treatment. However, limited information is available to evaluate if digital diabetic retinopathy surveillance systems in clinical practice actually result in an increase in the delivery of appropriate diagnosis and treatment of retinopathy in the diabetic population. In a retrospective observational study, we have assessed compliance with recommendations for further evaluation by an ophthalmologist based on recommendations made after review of DigiScope® images [20]. Over a 14-month study period, 1,453 consecutive patients with diabetes were evaluated with remote images obtained in their primary care physician’s office. Follow-up data were collected for patients with sight-threatening disease – namely, for the 52 (4%) patients for whom urgent referral was recommended. The majority of urgent referrals, 67%, were for proliferative diabetic retinopathy. Two patients were deceased at the time of fol- low-up. Of the remaining 50 patients, verification that an ophthalmic examination occurred was documented in 45 (90%) of the cases. Four of the five patients who did not follow through
with recommendations refused to see an ophthalmologist or did not keep their appointment, and one patient was lost to follow-up. Time to evaluation by an ophthalmologist varied from 1 to 319 days after imaging (mean 61 days). While it is recommended that urgent referrals be seen as soon as practical, and ideally within 1 week, only five patients were seen within this time frame after imaging. The time to evaluation includes an average of 24–48 h turnaround time between imaging and return of a report to the primary care physician. At least seven patients had documentation of laser treatment which was performed as a result of the referral recommendation, but information on treatment was not available for all patients. Since these patients were previously noncompliant with recommended diabetic eye care guidelines, it is most likely that their visionthreatening disease would not otherwise have been identified in a timely manner.
There are few studies specifically assessing the level of adherence with referral recommendations made by diabetic retinopathy assessment systems in the primary care setting. A recent study in an American Indian population found that, after implementation of telemedicine diabetic retinopathy surveillance technology, a 51% increase in the rate of laser treatment for diabetic retinopathy occurred over a 5-year period [21]. While not directly comparable to the DigiScope® platform described in this report, the United Kingdom national diabetic retinopathy screening program provides additional insight into compliance with consultation recommendations in “screen-positive” patients. At one program, 84% of 352 patients referred for diabetic retinopathy were evaluated by an ophthalmologist as recommended. However, only 33% with proliferative retinopathy were seen within 2 weeks as recommended [22]. A national survey of screening programs in the United Kingdom reported that almost half had waiting lists for patients who were identified as needing further assessment and treatment [23]. These examples demonstrate that implementation of diabetic retinopathy assessment programs may improve overall compliance with recommendations for further eye evaluations in patients with diabetes, but evaluations may not
