AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices.

BACKGROUND: Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging. OBJECTIVE: This study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD. METHODS: The Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process. RESULTS: Preliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024. CONCLUSIONS: This systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52602.

Toward safer ophthalmic artificial intelligence via distributed validation on real-world data.

PURPOSE OF REVIEW: The current article provides an overview of the present approaches to algorithm validation, which are variable and largely self-determined, as well as solutions to address inadequacies. RECENT FINDINGS: In the last decade alone, numerous machine learning applications have been proposed for ophthalmic diagnosis or disease monitoring. Remarkably, of these, less than 15 have received regulatory approval for implementation into clinical practice. Although there exists a vast pool of structured and relatively clean datasets from which to develop and test algorithms in the computational 'laboratory', real-world validation remains key to allow for safe, equitable, and clinically reliable implementation. Bottlenecks in the validation process stem from a striking paucity of regulatory guidance surrounding safety and performance thresholds, lack of oversight on critical postdeployment monitoring and context-specific recalibration, and inherent complexities of heterogeneous disease states and clinical environments. Implementation of secure, third-party, unbiased, pre and postdeployment validation offers the potential to address existing shortfalls in the validation process. SUMMARY: Given the criticality of validation to the algorithm pipeline, there is an urgent need for developers, machine learning researchers, and end-user clinicians to devise a consensus approach, allowing for the rapid introduction of safe, equitable, and clinically valid machine learning implementations.

Three-dimensional telesurgery and remote proctoring over a 5G network.

PURPOSE: To present 2 cases of vitreoretinal surgery performed on a three-dimensional (3D) heads-up display surgical platform with real-time transfer of 3D video over a fifth-generation (5G) cellular network. METHODS: An epiretinal membrane peel and tractional retinal detachment repair performed at Massachusetts Eye and Ear in April 2019 were broadcast live to the Verizon 5G Lab in Cambridge, MA. RESULTS: Both surgeries were successful. The heads-up digital surgery platform, combined with a 5G network, allowed telesurgical transfer of high-quality 3D vitreoretinal surgery with minimal degradation. Average end-to-end latency was 250 ms, and average round-trip latency was 16 ms. Fine surgical details were observed remotely by a proctoring surgeon and trainee, with real-time communication via mobile phone. CONCLUSIONS: This pilot study represents the first successful demonstration of vitreoretinal surgery transmitted over a 5G network. Telesurgery has the potential to enhance surgical education, provide intraoperative consultation and guidance from expert proctors, and improve patient outcomes, especially in remote and low-resource areas.

Application of Deep Learning for Diagnosing, Classifying, and Treating Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) affects nearly 200 million people and is the third leading cause of irreversible vision loss worldwide. Deep learning, a branch of artificial intelligence that can learn image recognition based on pre-existing datasets, creates an opportunity for more accurate and efficient diagnosis, classification, and treatment of AMD on both individual and population levels. Current algorithms based on fundus photography and optical coherence tomography imaging have already achieved diagnostic accuracy levels comparable to human graders. This accuracy can be further increased when deep learning algorithms are simultaneously applied to multiple diagnostic imaging modalities. Combined with advances in telemedicine and imaging technology, deep learning can enable large populations of patients to be screened than would otherwise be possible and allow ophthalmologists to focus on seeing those patients who are in need of treatment, thus reducing the number of patients with significant visual impairment from AMD.

Accelerating ophthalmic artificial intelligence research: the role of an open access data repository.

PURPOSE OF REVIEW: Artificial intelligence has already provided multiple clinically relevant applications in ophthalmology. Yet, the explosion of nonstandardized reporting of high-performing algorithms are rendered useless without robust and streamlined implementation guidelines. The development of protocols and checklists will accelerate the translation of research publications to impact on patient care. RECENT FINDINGS: Beyond technological scepticism, we lack uniformity in analysing algorithmic performance generalizability, and benchmarking impacts across clinical settings. No regulatory guardrails have been set to minimize bias or optimize interpretability; no consensus clinical acceptability thresholds or systematized postdeployment monitoring has been set. Moreover, stakeholders with misaligned incentives deepen the landscape complexity especially when it comes to the requisite data integration and harmonization to advance the field. Therefore, despite increasing algorithmic accuracy and commoditization, the infamous 'implementation gap' persists. Open clinical data repositories have been shown to rapidly accelerate research, minimize redundancies and disseminate the expertise and knowledge required to overcome existing barriers. Drawing upon the longstanding success of existing governance frameworks and robust data use and sharing agreements, the ophthalmic community has tremendous opportunity in ushering artificial intelligence into medicine. By collaboratively building a powerful resource of open, anonymized multimodal ophthalmic data, the next generation of clinicians can advance data-driven eye care in unprecedented ways. SUMMARY: This piece demonstrates that with readily accessible data, immense progress can be achieved clinically and methodologically to realize artificial intelligence's impact on clinical care. Exponentially progressive network effects can be seen by consolidating, curating and distributing data amongst both clinicians and data scientists.

Elective versus emergency abdominal surgery following cardiac transplantation: a Victorian state transplant service experience.

BACKGROUND: Heart transplant patients constitute a unique patient cohort with multiple risk factors predictive of poor surgical outcome. The Alfred Hospital offers the only heart transplant service in Victoria, Australia. This article presents The Alfred Hospital's experience with outcomes of abdominal operations in the heart transplant patient population. METHODS: The statewide cardiothoracic registry was cross-referenced with The Alfred Hospital's electronic hospital database to identify heart transplant patients who had undergone abdominal surgery from 2002 to November 2012. Patients who met the inclusion criteria were evaluated in two groups: elective and emergency surgical settings. In the emergency group, risk factors recorded for poor surgical outcome were high-dose immunosuppression therapy, diabetes and other conventional vascular risk factors. Outcome measures assessed in both groups were length of stay, readmission within 30 days and 1-year mortality. RESULTS: Twelve patients were identified who underwent 13 abdominal operations. Eight were elective cases and five were emergent abdominal operations. The mean length of stay was shorter in the elective group than the emergency group (2.5 days versus 21.3 days). There was one readmission within 30 days, and no mortality at 1 year following elective surgery. In the emergency surgery group, two patients were readmitted within 30 days post-operatively, and there were two deaths observed in this group. CONCLUSION: The Alfred Hospital experience demonstrates that elective abdominal surgery following heart transplantation can be performed safely. Emergent surgery in this group of patients, however, is associated with poorer outcomes.

Extracorporeal membrane oxygenation in primary graft failure after heart transplantation.

BACKGROUND: The aim of this review was to analyze our results with extracorporeal membrane oxygenation (ECMO) support for primary graft failure (PGF) in heart transplant recipients. METHODS: A retrospective review of 239 consecutive patients who underwent heart transplantation between January 2000 and August 2009 was performed. Orthotopic, heterotopic, and heart lung transplants were included in this analysis. Over that time period, 54 patients developed PGF, of whom 39 patients required ECMO support. These 39 patients form the basis of this review. RESULTS: Thirty-four patients (87%) were successfully weaned from ECMO and 29 (74.3%) survived to hospital discharge. There were no significant differences in wean rates or complications between central and peripheral ECMO. Comparison of survival in the 39 ECMO patients to the non-PGF patients (n = 185) showed a significantly worse survival in the ECMO group (p = 0.007). When those patients who died in the first 30 days were excluded, there was no difference in overall survival between groups (p = 0.73). CONCLUSIONS: Extracorporeal membrane oxygenation provides excellent circulatory support for patients with PGF after heart transplantation with good wean and survival to discharge rates.