Publications by authors named "Aditya U Kale"

Background: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals.

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Article Synopsis
  • * There is a need for thorough analysis of performance errors in AI medical devices, including issues like false correlations and specific failure modes, which can harm patients; guidelines for reporting these errors are not well-defined.
  • * This systematic review will evaluate how often and severely AI errors occur in randomized controlled trials (RCTs) of AI medical devices, as well as how performance errors are investigated, focusing on subgroup outcomes and adverse events.
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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.

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The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years.

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Purpose: Investigate the association between the optical coherence tomography angiography (OCTA) metrics derived from different analysis programs to understand the comparability of studies using these different approaches.

Methods: Secondary analysis of a prospective observational study (March 2018-September 2021). Forty-four right eyes and 42 left eyes from 44 patients were included.

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Article Synopsis
  • The article discusses how artificial intelligence (AI) is being used to screen and diagnose retinal diseases, highlighting its potential impact on telemedicine and healthcare systems, particularly in ophthalmology.
  • It reviews recent studies on AI algorithms for retinal disease and outlines four essential factors for their effective use: managing large data sets, ensuring practical application in eye care, adhering to regulations, and balancing costs and profitability.
  • The Vision Academy acknowledges the pros and cons of AI technologies and provides recommendations for future advancements in this area.
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Article Synopsis
  • - The review aims to outline the latest advancements in AI devices for managing retinal conditions and offers recommendations from the Vision Academy.
  • - Despite the promising benefits of AI models for personalized treatments and risk scoring, most have not yet received regulatory approval, raising concerns about their application and safety in diverse patient populations.
  • - As AI medical devices continue to develop, current clinical practices may need to adapt, emphasizing the importance of reaching a consensus on their safety and effectiveness for widespread use.
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Purpose: Diabetic retinopathy (DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset.

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Purpose Of Review: In this review, we consider the challenges of creating a trusted resource for real-world data in ophthalmology, based on our experience of establishing INSIGHT, the UK's Health Data Research Hub for Eye Health and Oculomics.

Recent Findings: The INSIGHT Health Data Research Hub maximizes the benefits and impact of historical, patient-level UK National Health Service (NHS) electronic health record data, including images, through making it research-ready including curation and anonymisation. It is built around a shared 'north star' of enabling research for patient benefit.

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Uveitis consists of a group of syndromes characterised by intraocular inflammation, accounting for up to 15% of visual loss in the western world and 10% worldwide. Assessment of intraocular inflammation has been limited to clinician-dependent, subjective grading. Developments in imaging technology, such as optical coherence tomography (OCT), have enabled the development of objective, quantitative measures of inflammatory activity.

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Purpose: Vitreous haze (VH) is a key marker of inflammation in uveitis but limited by its subjectivity. Optical coherence tomography (OCT) has potential as an objective, noninvasive method for quantifying VH. We test the hypotheses that OCT can reliably quantify VH and the measurement is associated with slit-lamp based grading of VH.

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To assess the stability of retinal structure and blood flow measures over time and in different clinical settings using portable optical coherence tomography angiography (OCTA) as a potential biomarker of central perfusion in critical illness, 18 oesophagectomy patients completed retinal structure and blood flow measurements by portable OCT and OCTA in the eye clinic and intensive therapy unit (ITU) across three timepoints: (1) pre-operation in a clinic setting; (2) 24-48 h post-operation during ITU admission; and (3) seven days post-operation, if the patient was still admitted. Blood flow and macular structural measures were stable between the examination settings, with no consistent variation between pre- and post-operation scans, while retinal nerve fibre layer thickness increased in the post-operative scans (+2.31 µm, = 0.

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A 32-year-old doctor, who has a medical history of primary Raynaud's disease and previous scotomas, presented to eye clinic with sudden onset blurring of vision (infero-nasally) with no other associated symptoms. The patient had good visual acuity bilaterally (6/6) and no anterior chamber activity or conjunctival hyperaemia. Findings consistent with a nerve fibre layer infarct were noted in the right eye, with unremarkable examination of the left eye.

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Objective: This study aims to evaluate the feasibility of retinal imaging in critical care using a novel mobile optical coherence tomography (OCT) device. The Heidelberg SPECTRALIS FLEX module (Heidelberg Engineering, Heidelberg, Germany) is an OCT unit with a boom arm, enabling ocular OCT assessment in less mobile patients.

Design: We undertook an evaluation of the feasibility of using the SPECTRALIS FLEX for undertaking ocular OCT images in unconscious and critically ill patients.

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Article Synopsis
  • Deep learning algorithms show significant potential in medical diagnostics, prompting a systematic review to evaluate their accuracy against healthcare professionals in classifying diseases through medical imaging.
  • From 31,587 studies screened, 82 met the criteria for inclusion, where sensitivity of deep learning methods ranged from 9.7% to 100% (mean 79.1%), and specificity ranged from 38.9% to 100% (mean 88.3%).
  • In a subset of studies comparing deep learning with healthcare professionals, the pooled sensitivity for deep learning models was 87% compared to 86.4% for professionals, indicating comparable diagnostic performance.
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Purpose: New instrument-based techniques for anterior chamber (AC) cell counting can offer automation and objectivity above clinician assessment. This review aims to identify such instruments and its correlation with clinician estimates.

Methods: Using standard systematic review methodology, we identified and tabulated the outcomes of studies reporting reliability and correlation between instrument-based measurements and clinician AC cell grading.

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