MERCI: a machine learning approach to identifying hydroxychloroquine retinopathy using mfERG

Habib, Faisal; Huang, Huaxiong; Gupta, Arvind; Wright, Tom

doi:10.1007/s10633-022-09879-7

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Patients referred for mfERG screening for possible HCQ retinopathy were assessed using ring average response thresholds and ring ratios. Ring ratios were assessed for abnormality by comparison with published thresholds [ 5 , 6 ]. Patients with abnormal electrophysiology results were identified by retrospective chart review.…”

Section: Methodsmentioning

confidence: 99%

The effect of COVID-19 on referral patterns for clinical electrophysiological testing

2022

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Purpose To provide an overview of the effect that the COVID-19 pandemic has had on visual electrophysiology referral patterns and the subsequent effect this may have on patients. Methods All electrodiagnostic tests performed at Kensington Vision and Research Centre, Toronto Canada, in a 3-month period prior to the COVID-19 pandemic (1 September 2019 to 30 November 2019) were compared to a 3-month period after the start of the COVID-19 pandemic (1 September 2021 to 30 November 2021). Results A total of 502 patients had electrodiagnostic testing carried out in the designated time periods: 292 in the time period prior to the COVID-19 pandemic and 210 patients after. There was a significant change in the reason for referral in patients pre-COVID compared to post-COVID ( p = 0.004). There was a 43% reduction in referrals for drug monitoring, 25% reduction for hereditary pathology and a 27% increase in acquired pathology after the start of the COVID-19 pandemic compared to before. Conclusions There was a substantial decrease in the total number of patients referred after the start of the COVID-19 pandemic compared to pre-COVID with inherited retinal pathology and drug monitoring patients being 2 populations most affected by the disruption to healthcare services.

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Section: Methodsmentioning

confidence: 99%

The effect of COVID-19 on referral patterns for clinical electrophysiological testing

2022

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“…Artificial intelligence (AI) is being applied to many areas of healthcare showing high levels of accuracy, equivalent to experts. There have been investigations applying AI or machine-learning techniques to electrophysiology data [34,[85][86][87][88][89][90]. These include studies of ERG data that may have applicability in conditions including glaucoma [88], hydroxychloroquine retinopathy [87], and even autism spectrum disorder [86] and depression [89], well as studies of VEP data to improve estimation of acuity [90].…”

Section: Mathematical Models Of Phototransduction and Outer Retinal C...mentioning

confidence: 99%

“…There have been investigations applying AI or machine-learning techniques to electrophysiology data [34,[85][86][87][88][89][90]. These include studies of ERG data that may have applicability in conditions including glaucoma [88], hydroxychloroquine retinopathy [87], and even autism spectrum disorder [86] and depression [89], well as studies of VEP data to improve estimation of acuity [90]. A recent study demonstrated machine learning techniques could be successfully applied to develop automated ERG phenotypic classification of patients with Stargardt disease (arising from variants in the ABCA4 gene) [85].…”

Section: Mathematical Models Of Phototransduction and Outer Retinal C...mentioning

confidence: 99%

Visual electrophysiology and “the potential of the potentials”

Mahroo

2023

Eye

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Visual electrophysiology affords direct, quantitative, objective assessment of visual pathway function at different levels, and thus yields information complementary to, and not necessarily obtainable from, imaging or psychophysical testing. The tests available, and their indications, have evolved, with many advances, both in technology and in our understanding of the neural basis of the waveforms, now facilitating more precise evaluation of physiology and pathophysiology. After summarising the visual pathway and current standard clinical testing methods, this review discusses, non-exhaustively, several developments, focusing particularly on human electroretinogram recordings. These include new devices (portable, non-mydiatric, multimodal), novel testing protocols (including those aiming to separate rod-driven and cone-driven responses, and to monitor retinal adaptation), and developments in methods of analysis, including use of modelling and machine learning. It is likely that several tests will become more accessible and useful in both clinical and research settings. In future, these methods will further aid our understanding of common and rare eye disease, will help in assessing novel therapies, and will potentially yield information relevant to neurological and neuro-psychiatric conditions.

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A Comprehensive Assessment of Environmental Implications, Ecotoxicity and Bioaccumulation Potential of Repurposed Drug Hydroxychloroquine: From Challenges to Sustainability

Gupta,

Rani

2024

Reviews Env.Contamination (formerly:Residue Reviews)

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MERCI: a machine learning approach to identifying hydroxychloroquine retinopathy using mfERG

Cited by 4 publications

References 24 publications

The effect of COVID-19 on referral patterns for clinical electrophysiological testing

The effect of COVID-19 on referral patterns for clinical electrophysiological testing

Visual electrophysiology and “the potential of the potentials”

A Comprehensive Assessment of Environmental Implications, Ecotoxicity and Bioaccumulation Potential of Repurposed Drug Hydroxychloroquine: From Challenges to Sustainability

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