Automated Diabetic Retinopathy Diagnosis for Improved Clinical Decision Support.

We report on the prediction performance of artificial intelligence components embedded into a telehealth platform underlying a newly established eye screening service connecting metropolitan-based ophthalmologists to patients in remote indigenous communities in Northern Territory and Queensland. Two AI-based components embedded into the telehealth platform were evaluated on retinal images collected from 328 unique patients: an image quality alert system and a diabetic retinopathy detection system. Compared to ophthalmologists, at an individual image level, the image quality detection algorithm was correct 72% of the time, and 85% accurate at a patient level. The retinopathy detection algorithm was correct 85% accurate at an individual image level, and 87% accurate at a patient level. This evaluation provides assurances for future service models using AI to complement and support decisions of eye health assessment teams.

A Deep Learning-Based System for the Assessment of Dental Caries Using Colour Dental Photographs.

D1ental caries remains the most common chronic disease in childhood, affecting almost half of all children globally. Dental care and examination of children living in remote and rural areas is an ongoing challenge that has been compounded by COVID. The development of a validated system with the capacity to screen large numbers of children with some degree of automation has the potential to facilitate remote dental screening at low costs. In this study, we aim to develop and validate a deep learning system for the assessment of dental caries using color dental photos. Three state-of-the-art deep learning networks namely VGG16, ResNet-50 and Inception-v3 were adopted in the context. A total of 1020 child dental photos were used to train and validate the system. We achieved an accuracy of 79% with precision and recall respectively 95% and 75% in classifying 'caries' versus 'sound' with inception-v3.

A fast and fully automated system for glaucoma detection using color fundus photographs.

This paper presents a low computationally intensive and memory efficient convolutional neural network (CNN)-based fully automated system for detection of glaucoma, a leading cause of irreversible blindness worldwide. Using color fundus photographs, the system detects glaucoma in two steps. In the first step, the optic disc region is determined relying upon You Only Look Once (YOLO) CNN architecture. In the second step classification of 'glaucomatous' and 'non-glaucomatous' is performed using MobileNet architecture. A simplified version of the original YOLO net, specific to the context, is also proposed. Extensive experiments are conducted using seven state-of-the-art CNNs with varying computational intensity, namely, MobileNetV2, MobileNetV3, Custom ResNet, InceptionV3, ResNet50, 18-Layer CNN and InceptionResNetV2. A total of 6671 fundus images collected from seven publicly available glaucoma datasets are used for the experiment. The system achieves an accuracy and F1 score of 97.4% and 97.3%, with sensitivity, specificity, and AUC of respectively 97.5%, 97.2%, 99.3%. These findings are comparable with the best reported methods in the literature. With comparable or better performance, the proposed system produces significantly faster decisions and drastically minimizes the resource requirement. For example, the proposed system requires 12 times less memory in comparison to ResNes50, and produces 2 times faster decisions. With significantly less memory efficient and faster processing, the proposed system has the capability to be directly embedded into resource limited devices such as portable fundus cameras.

Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiographs.

OBJECTIVE: This study aimed to evaluate a deep learning (DL) system using convolutional neural networks (CNNs) for automatic detection of caries on bitewing radiographs. STUDY DESIGN: In total, 2468 bitewings were labeled by 3 dentists to create the reference standard. Of these images, 1257 had caries and 1211 were sound. The Faster region-based CNN was applied to detect the regions of interest (ROIs) with potential lesions. A total of 13,246 ROIs were generated from all 'sound' images, and 50% of 'caries' images (selected randomly) were used to train the ROI detection module. The remaining 50% of 'caries' images were used to validate the ROI detection module. Caries detection was then performed using Inception-ResNet-v2. A set of 3297 'caries' and 5321 'sound' ROIs cropped from the 2468 images was used to train and validate the caries detection module. Data sets were randomly divided into training (90%) and validation (10%) data sets. Recall, precision, specificity, accuracy, and F1 score were used as metrics to assess performance. RESULTS: The caries detection module achieved recall, precision, specificity, accuracy, and F1 scores of 0.89, 0.86, 0.86, 0.87, and 0.87, respectively. CONCLUSIONS: The proposed DL system demonstrated promising performance for detecting proximal surface caries on bitewings.

Mobile photographic screening for dental caries in children: Diagnostic performance compared to unaided visual dental examination.

OBJECTIVES: This study was conducted to compare the use of intraoral photographs with the unaided visual dental examination as a means of dental caries detection in children. METHODS: Children aged 4- to 14-year-olds were visually examined at their schools. Following dental examinations, children had five photographs of their teeth taken using a smartphone camera. Four dental reviewers, who are different from those who visually examined the children, assessed intraoral photographs for dental caries. Sensitivity, specificity, and inter-rater reliability agreement were estimated to assess the diagnostic performance of the photographic method relative to the benchmark visual dental assessments. Caries prevalence was measured using dft/DFT (decayed and filled teeth) index. RESULTS: One hundred thirty-eight children (67 male and 71 female) were enrolled and had a mean age of 7.8 ± 2.1 years. The caries prevalence (dft/DFT > 0) using photographic dental assessments ranged from 30 percent to 39 percent but was not significantly different from the prevalence (42 percent) estimated with the visual dental examination (P ≥ 0.07). The sensitivity and specificity of the photographic method for detection of dental caries compared to visual dental assessments were 58-80 percent and 99.7-99.9 percent, respectively. The sensitivity for the photographic assessments was high in the primary dentition (63-82 percent) and children ≤7-year-olds (67-78 percent). The inter-rater reliability for the photographic assessment versus the benchmark ranged from substantial to almost perfect agreement (Kappa = 0.72-0.87). CONCLUSIONS: The photographic approach to dental screening, used within the framework of its limitations, yielded an acceptable diagnostic level of caries detection, particularly in younger children with primary dentition.

Deep learning for automated detection and numbering of permanent teeth on panoramic images.

OBJECTIVE: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). METHODS: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. RESULTS: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. CONCLUSION: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

Capillary vascular density in the retina of hypertensive patients is associated with a non-dipping pattern independent of mean ambulatory blood pressure.

BACKGROUND: A nocturnal non-dipping pattern has been associated with hypertension-mediated organ damage (HMOD), morbidity and mortality. Retinal imaging through application of modern technologies including optical coherence tomography angiography (OCT-A) can provide detailed insights into early vascular damage. In this observational study, we investigated the relationship of microscopic vascular density in the retina measured with OCT-A and nocturnal blood pressure (BP) dipping. METHODS: Retinal OCT-A and ambulatory BP monitoring (ABPM) data prospectively obtained from 142 patients referred to a tertiary hypertension clinic were analysed with regression models for associations between BP night-time dipping and retinal capillary vascular density in three different zones around the fovea. RESULTS: More pronounced nocturnal SBP and DBP dipping was significantly associated with increased vascular density in the central foveal area of the retina. These associations were robust to adjustment for other available risk factors including mean daytime BP. Parafoveal and whole image vascular density did not show equivalent significant associations with nocturnal BP dipping. The results were reproducible when assessed in a subgroup of patients who had concomitant type 2 diabetes. CONCLUSION: Foveal vascular density was associated with the nocturnal BP dipping pattern in hypertensive patients. These associations were robust to adjustment of relevant factors such as daytime BP. Our findings highlight the importance of nocturnal BP features reflected in ambulatory BP monitoring in the assessment of HMOD. Whether routine assessment of retinal damage markers may improve risk management of hypertensive patients remains to be determined.

Retinal capillary rarefaction is associated with arterial and kidney damage in hypertension.

Microvascular disease and rarefaction are key pathological hallmarks of hypertension. The retina uniquely allows direct, non-invasive investigation of the microvasculature. Recently developed optical coherence tomography angiography now allows investigation of the fine retinal capillaries, which may provide a superior marker of overall vascular damage. This was a prospective cross-sectional study to collect retinal capillary density data on 300 normal eyes from 150 hypertensive adults, and to investigate possible associations with other organ damage markers. The average age of participants was 54 years and there was a greater proportion of males (85; 57%) than females. Multivariate, confounder adjusted linear regression showed that retinal capillary rarefaction in the parafovea was associated with increased pulse wave velocity (ß = - 0.4, P = 0.04), log-albumin/creatinine ratio (ß = - 0.71, P = 0.003), and with reduced estimated glomerular filtration rate (ß = 0.04, P = 0.02). Comparable significant associations were also found for whole-image vascular-density, for foveal vascular-density significant associations were found with pulse wave velocity and estimated glomerular filtration rate only. Our results indicate that retinal capillary rarefaction is associated with arterial stiffness and impaired kidney function. Retinal capillary rarefaction may represent a useful and simple test to assess the integrated burden of hypertension on the microvasculature irrespective of current blood pressure levels.

Microvascular changes at different stages of chronic kidney disease.

Patients with progressing chronic kidney disease (CKD) are more likely to experience cardio- and cerebrovascular events than progressing to end-stage renal disease. The authors explored whether retinal microvascular calibers differed with the degree of renal impairment and between the standard and extended optic disk and may serve as a simple additional tool for risk stratification in this highly vulnerable patient cohort. The authors analyzed central retinal arteriolar and venular equivalent calibers (CRAE, CRVE) at different retinal zones (zone B&C) using digital retinal imaging in hypertensive patients with stage 2 (n = 66) or stage 3 CKD (n = 30). Results were adjusted for age, sex, HbA1c, and 24-hour diastolic blood pressure. Mean eGFR was 77.7 ± 8.9 and 48.8 ± 7.9 ml/min/1.73 m2 for stage 2 and 3 CKD, respectively. CRAE and CRVE in zones B and C were significantly lower in patients with stage 3 CKD compared to patients with stage 2 CKD (CRAE-B:141.1 ± 21.4 vs. 130.5 ± 18.9 µm, p = .030; CRAE-C:137.4 ± 19.4 vs 129.2 ± 18.2 µm, p = .049; CRVE-B:220.8 ± 33.0 vs. 206.0 ± 28.4 µm, p = .004; and CRVE-C:215.9 ± 33.0 vs. 201.2 ± 25.1µm, p = .003). In patients with stage 2 CKD, CRAE-B was higher than CRAE-C (141.1 ± 21.4 vs. 137.4 ± 19.4µm, p < .001). In contrast, such a difference was not found in patients with stage 3 CKD. CRAE of both retinal zones correlated with eGFR for the entire cohort. In patients with stage 3 CKD, retinal narrowing is more pronounced compared to patients with stage 2 CKD. Whether the novel observation of difference in arteriolar caliber between zones B and C in stage 2 CKD could serve as an early marker of CKD progression warrants further investigation.

Teledentistry as a novel pathway to improve dental health in school children: a research protocol for a randomised controlled trial.

BACKGROUND: Despite great improvement in child oral health, some children subgroups still suffer from higher levels of dental caries. Geographic and socioeconomic barriers and the lack of access to dental care services are among common reasons for poor oral health in children. Historically in Australia, oral health therapists or dental therapists have been responsible for providing dental care for school children through the School Dental Services (SDS). The current SDS has been unable to provide sustainable dental care to all school children due to a reduction in workforce participation and limited resources. We propose a paradigm shift in the current service through the introduction of user-friendly technology to provide a foundation for sustainable dental care for school children. METHODS/DESIGN: We describe an ongoing parallel, two-armed, non-inferiority randomised controlled trial that compares routine and teledental pathway of dental care in children aged 4-15 years (n = 250). Participating schools in Western Australia will be randomly assigned to the control or teledental group, approximately three schools in each group with a maximum of 45 children in each school. All participants will first receive a standard dental examination to identify those who require urgent referrals and then their teeth will be photographed using a smartphone camera. At the baseline, children in the control group will receive screening results and advice on the pathway of dental care based on the visual dental screening while children in the teledental group will receive screening results based on the assessment of dental images. At 9 months follow-up, all participants will undergo a final visual dental screening. The primary outcomes include decay experience and proportion of children become caries active. The secondary outcomes include the diagnostic performance of photographic dental assessment and costs comparison of two pathways of dental care. DISCUSSION: The current project seeks to take advantage of mobile technology to acquire dental images from a child's mouth at school settings and forwarding images electronically to an offsite dental practitioner to assess and prepare dental recommendations remotely. Such an approach will help to prioritise high-risk children and provide them with a quick treatment pathway and avoid unnecessary referrals or travel. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12619001233112. Registered 06 September 2019.

Effect of Pupil Dilation with Tropicamide on Retinal Vascular Caliber.

Purpose: The retinal blood vessels reflect changes in the brain's micro-circulation and these changes have been shown to correlate with the incidence of diseases such as stroke, heart disease and Alzheimer's disease. Studies investigating the retinal vasculature routinely use pupil dilation with tropicamide to optimize image acquisition and quality. The aim of this study was to investigate the effects of tropicamide on retinal vascular parameters using retinal photography.Methods: The study was performed on 41 healthy young subjects of both sexes, using tropicamide to dilate only the right pupil, leaving the left as a control.Results: Pupil dilation with tropicamide resulted in reduced retinal vessel width measures based on standardized approaches, particularly reduced arteriolar caliber (p < .0005). However, closer investigation of the images revealed reduced fundus image magnification in the post-tropicamide images, based on reduced optic nerve head diameter (p < .0005) and longitudinal analysis with image registration and affine transformation (p < .0001). No change in vessel width parameters was observed after adjustment for image magnification.Conclusion: These results suggest that tropicamide does not change the width of the retinal vessels, however width parameters as measured by standard approaches may be reduced due to image magnification changes resulting from cycloplegia. In this study, improved optic nerve head segmentation for image scale conversion removed the magnification error. With this correction, the tropicamide intervention had no effect on vessel width parameters in young healthy people and could be utilized in future without affecting the results of retinal vascular analysis.

A Mobile Phone Informational Reminder to Improve Eye Care Adherence Among Diabetic Patients in Rural China: A Randomized Controlled Trial.

PURPOSE: To determine whether short message service (SMS) reminders improve adherence to scheduled ocular examinations among patients with diabetes in rural China. DESIGN: Randomized controlled trial. METHODS: This study enrolled consecutive patients with diabetes scheduled for eye examinations at 5 hospitals in low-income areas of Guangdong, China from March 1, 2015 to May 31, 2016. Participants were randomized (1:1) to receive automated SMS reminders containing information about diabetic retinopathy (DR) 1 week and 3 days prior to scheduled eye appointments (Intervention) or to appointments without reminders (Control). Regression models following intention-to-treat principles were used to estimate the association between the main outcome (attendance within ± 1 week of scheduled visit) and membership in the Intervention group, with and without adjustment for other potential predictors of follow-up. Secondary outcomes included change in DR knowledge score (1, worst; 5, best) and endline satisfaction with care (3, worst; 15, best). RESULTS: Among 233 patients, 119 (51.1%) were randomized to Intervention (age 59.7 ± 11.3 years, 52.1% men) and 114 (48.9%) to Control (58.7 ± 9.50 years, 49.1% men). All participants provided data for the main study outcome. Attendance at scheduled appointments for the Intervention group (51/119, [42.9%]) was significantly higher than for Controls (16/114, [14.0%], between-group difference 28.8% [95% confidence interval (CI) 17.9%, 39.8%], P < .001). Factors associated with attendance in multiple regression models included Intervention group membership (Relative Risk [RR] 3.04, 95% CI, 1.73-5.33, P < .001) and baseline DR knowledge (RR 1.47, 95% CI 1.21-1.78, P < .001). Improvement in Satisfaction (mean difference 1.08, 95% CI 0.70-1.46, P < .001) and DR knowledge (mean difference 1.30, 95% CI 0.96-1.63, P < .001) were significantly higher for the Intervention group. Total cost of the intervention was US$5.40/person. CONCLUSION: Low-cost SMS informational reminders significantly improved adherence to, knowledge about, and satisfaction with care. Additional interventions are needed to further improve adherence.

Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care.

Importance: There has been wide interest in using artificial intelligence (AI)-based grading of retinal images to identify diabetic retinopathy, but such a system has never been deployed and evaluated in clinical practice. Objective: To describe the performance of an AI system for diabetic retinopathy deployed in a primary care practice. Design, Setting, and Participants: Diagnostic study of patients with diabetes seen at a primary care practice with 4 physicians in Western Australia between December 1, 2016, and May 31, 2017. A total of 193 patients consented for the study and had retinal photographs taken of their eyes. Three hundred eighty-six images were evaluated by both the AI-based system and an ophthalmologist. Main Outcomes and Measures: Sensitivity and specificity of the AI system compared with the gold standard of ophthalmologist evaluation. Results: Of the 193 patients (93 [48%] female; mean [SD] age, 55 [17] years [range, 18-87 years]), the AI system judged 17 as having diabetic retinopathy of sufficient severity to require referral. The system correctly identified 2 patients with true disease and misclassified 15 as having disease (false-positives). The resulting specificity was 92% (95% CI, 87%-96%), and the positive predictive value was 12% (95% CI, 8%-18%). Many false-positives were driven by inadequate image quality (eg, dirty lens) and sheen reflections. Conclusions and Relevance: The results demonstrate both the potential and the challenges of using AI systems to identify diabetic retinopathy in clinical practice. Key challenges include the low incidence rate of disease and the related high false-positive rate as well as poor image quality. Further evaluations of AI systems in primary care are needed.

Retinal hemorrhage detection by rule-based and machine learning approach.

Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

Content Design and System Implementation of a Teleophthalmology System for Eye Disease Diagnosis and Treatment and Its Preliminary Practice in Guangdong, China.

BACKGROUND: We have developed a new telemedicine system for comprehensive eye examination, diabetic retinopathy (DR) screening, and eye disease diagnosis and treatment. The novel points of the system include a tablet application for facilitating doctor's examination and diagnosis process, a comprehensive eye examination component, and integrated treatment planning and recording. METHODS: The system provided a new service model through one ophthalmological center linking with multiple remote and rural hospitals for eye care in Guangdong province, China. RESULTS: The early stage of the project study also undertook the responsibility of educations for remote-area doctors and image graders for DR grading and glaucoma grading and research on the effectiveness of short message service (SMS) reminder for patient revisit. Some other research, such as the comparison of the accuracy of graders' DR grading with the gold standard, and doctor's tentative diagnosis with final diagnosis and related statistical information, has been implemented in the system. In the preliminary practice, we summarized the outcomes related to presenting system performance and made an initial analysis. CONCLUSION: From the practice, the project has shown the telemedicine system and associated contents have satisfied our initial goal and demonstrated their effectiveness and efficiency.

Utility of Ward-Based Retinal Photography in Stroke Patients.

BACKGROUND: Improvements in acute care of stroke patients have decreased mortality, but survivors are still at increased risk of future vascular events and mitigation of this risk requires thorough assessment of the underlying factors leading to the stroke. The brain and eye share a common embryological origin and numerous similarities exist between the small vessels of the retina and brain. Recent population-based studies have demonstrated a close link between retinal vascular changes and stroke, suggesting that retinal photography could have utility in assessing underlying stroke risk factors and prognosis after stroke. Modern imaging equipment can facilitate precise measurement and monitoring of vascular features. However, use of this equipment is a challenge in the stroke ward setting as patients are frequently unable to maintain the required seated position, and pupil dilatation is often not feasible as it could potentially obscure important neurological signs of stroke progression. MATERIALS AND METHODS: This small study investigated the utility of a novel handheld, nonmydriatic retinal camera in the stroke ward and explored associations between retinal vascular features and stroke risk factors. This camera circumvented the practical limitations of conducting retinal photography in the stroke ward setting. RESULTS: A positive correlation was found between carotid disease and both mean width of arterioles (r = .40, P = .00571) and venules (r = .30, P = .0381). CONCLUSIONS: The results provide further evidence that retinal vascular features are clinically informative about underlying stroke risk factors and demonstrate the utility of handheld retinal photography in the stroke ward.

End-user acceptance of a cloud-based teledentistry system and Android phone app for remote screening for oral diseases.

Objective This study aimed to evaluate users' acceptance of a teledentistry model utilizing a smartphone camera used for dental caries screening and to identify a number of areas for improvement of the system. Methods A store-and-forward telemedicine platform "Remote-I" was developed to assist in the screening of oral diseases using an image acquisition Android app operated by 17 teledental assistants. A total of 485 images (five images per case) were directly transmitted from the Android app to the server. A panel of five dental practitioners (graders) assessed the images and reported their diagnosis. A user acceptance survey was sent to the graders and smartphone users following completion of the screening program. Results Of the 22 surveys sent out, 20 (91%) were completed. Generally, users showed optimism towards the use of the teledentistry system, and strongly positively assessed items on content and service quality. The majority of graders took less than 15 min to read the images while phone users took 5-10 min to complete the dental photography using the Android app. This study identified a number of factors that are essential for improving the current system, such as optimization of smartphone camera features, the format of the server, and the orientation of images and using oral retractors during photography. Conclusions Users appear to be generally satisfied with the proposed teledentistry model. However, they have specific concerns to address, many of which could be resolved through more effective training, coordination between sites and upgrading the current system.

A proof-of-concept evaluation of a cloud-based store-and-forward telemedicine app for screening for oral diseases.

OBJECTIVE: It is widely considered that telemedicine can make positive contributions to dental practice. This study aimed to evaluate a cloud-based telemedicine application for screening for oral diseases. METHODS: A telemedicine system, based on a store-and-forward method, was developed to work as a platform for data storage. An Android application was developed to facilitate entering demographic details and capturing oral photos. As a proof-of-concept, six volunteers were enrolled in a trial to obtain oral images using smartphone cameras. Following an onsite oral examination, images of participants' teeth were obtained by a trained dental assistant. Oral images were directly uploaded from the smartphone to a cloud-based server via broadband network. The assessments of oral images by offsite dentists were compared with those carried out via face-to-face oral examinations. RESULTS: A complete set of 30 oral images was obtained from all six participants. Out of 192 teeth reviewed, the proportion of ungradable teeth was 8%. Sensitivity and specificity of teledental screening were 57% and 100% respectively. The inter-grader agreement estimated for two examination modalities and between two teledental graders was 70% and 62% respectively. Findings indicate that the proposed system for screening of oral diseases can be implemented to provide a valid and reliable alternative to traditional oral screening. CONCLUSION: This study provided evidence that a robust system for store-and-forward screening for dental problems can be developed, and leads to the need for further testing of its robustness to confirm the accuracy and reliability of the teledentistry system.

Development and Practice of Store-and-Forward Telehealth Systems in Ophthalmology Dental and Emergency.

Store-and-forward (S&F) telehealth system has been becoming an increasing application in remote medical consultations. In this paper, we will introduce three novel S&F telehealth systems we developed for ophthalmological, dental and emergency applications. We will explain the general system architecture of the S&F systems. Then we will focus on the specific features and components in each system implemented for meeting their respective clinical requirements. In the final section we will present further implementation details and practices and provide discussions.

Establishing an indigenous tele-eye care service.

We describe the establishment of a new tele-eye care solution over a Broadband Satellite service, connecting metropolitan-based ophthalmologists to patients with eye conditions in rural and remote Australia. A trial of the service demonstrates the ability of the telehealth system to close the gap in access to eye care services for adult Australians living in under-served rural and remote communities, preventing needless blindness.