Early diabetic eye damage: Comparing detection methods using diagnostic power

Rai, Bhim B.; van Kleef, Joshua P.; Sabeti, Faran; Vlieger, Robin; Suominen, Hanna; Maddess, Ted

doi:10.1016/j.survophthal.2023.09.002

Cited by 5 publications

(10 citation statements)

References 85 publications

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“…A study by Montesanto et al. ( 15 ) showed significant loss relative to normal controls using Matrix; however, in our calculation using the published data, the AUROC was only around 60% ( 31 ). Other reports have shown no significant change in SWAP mean deviations in PwT2D ( 8 , 42 ).…”

Section: Discussioncontrasting

confidence: 62%

“…A recent review of 44 functional and structural measures from 23 studies, which examined diagnostic power for discriminating noDR eyes from control eyes using a range of structural and functional methods, found median AUROCs around 89% for OFA and 60% to 70% for the other methods (p < 0.0001). That review included a recent OFA study of young persons with type 1 diabetes, and the overall median value across nine measures from four OFA studies was 89% ( 31 ). That included a recent study using new fifth-generation OFA stimuli which test both eyes in <90 s ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, these methods require long setup times ( 29 ) and have exhibited high inter-subject variability ( 30 ). Also, the diagnostic power of these methods in diabetic persons who show no retinopathy is poor ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

“…MfPOP delays are also a good marker of progression or improvement of DMO, and better than Matrix perimetry ( 32 , 34 ). A recent review of 44 functional and structural measures from 23 studies indicates that mfPOP measures are significantly better at discriminating normal controls from persons with diabetes without evidence of classical retinopathy ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

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Discriminating early-stage diabetic retinopathy with subjective and objective perimetry

Sabeti,

van Kleef,

Iyer

et al. 2024

Front. Endocrinol.

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IntroductionTo prevent progression of early-stage diabetic retinopathy, we need functional tests that can distinguish multiple levels of neural damage before classical vasculopathy. To that end, we compared multifocal pupillographic objective perimetry (mfPOP), and two types of subjective automated perimetry (SAP), in persons with type 2 diabetes (PwT2D) with either no retinopathy (noDR) or mild to-moderate non-proliferative retinopathy (mmDR).MethodsBoth eyes were assessed by two mfPOP test methods that present stimuli within either the central ±15° (OFA15) or ±30° (OFA30), each producing per-region sensitivities and response delays. The SAP tests were 24-2 Short Wavelength Automated Perimetry and 24-2 Matrix perimetry.ResultsFive of eight mfPOP global indices were significantly different between noDR and mmDR eyes, but none of the equivalent measures differed for SAP. Per-region mfPOP identified significant hypersensitivity and longer delays in the peripheral visual field, verifying earlier findings. Diagnostic power for discrimination of noDR vs. mmDR, and normal controls vs. PwT2D, was much higher for mfPOP than SAP. The mfPOP per-region delays provided the best discrimination. The presence of localized rather than global changes in delay ruled out iris neuropathy as a major factor.DiscussionmfPOP response delays may provide new surrogate endpoints for studies of interventions for early-stage diabetic eye damage.

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Section: Discussioncontrasting

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminating early-stage diabetic retinopathy with subjective and objective perimetry

Sabeti,

van Kleef,

Iyer

et al. 2024

Front. Endocrinol.

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“…Longitudinal studies have shown that SWAP can detect VF defects 3-5 years earlier than full-threshold SAP [36,37]. SAWP is reported to be useful in DRD [38]. SWAP is limited by the yellow colour of early cataracts and acts as a blue filter causing notable diffuse reduction in sensitivity.…”

Section: Short Wavelength Automated Perimetry (Swap)mentioning

confidence: 99%

Visual Field Tests: A Narrative Review of Different Perimetric Methods

Rai,

Sabeti,

Carle

et al. 2024

JCM

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Visual field (VF) testing dates back to fifth century B.C. It plays a pivotal role in the diagnosis, management, and prognosis of retinal and neurological diseases. This review summarizes each of the different VF tests and perimetric methods, including the advantages and disadvantages and adherence to the desired standard diagnostic criteria. The review targets beginners and eye care professionals and includes history and evolution, qualitative and quantitative tests, and subjective and objective perimetric methods. VF testing methods have evolved in terms of technique, precision, user-friendliness, and accuracy. Consequently, some earlier perimetric techniques, often still effective, are not used or have been forgotten. Newer technologies may not always be advantageous because of higher costs, and they may not achieve the desired sensitivity and specificity. VF testing is most often used in glaucoma and neurological diseases, but new objective methods that also measure response latencies are emerging for the management of retinal diseases. Given the varied perimetric methods available, clinicians are advised to select appropriate methods to suit their needs and target disease and to decide on applying simple vs. complex tests or between using subjective and objective methods. Newer, rapid, non-contact, objective methods may provide improved patient satisfaction and allow for the testing of children and the infirm.

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Multi‐Deep Learning Approach With Transfer Learning for 7‐Stages Diabetic Retinopathy Classification

Khan,

Sun,

Khalid

et al. 2024

Int J Imaging Syst Tech

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Proposed novel investigation focused on leveraging an innovative diabetic retinopathy (DR) dataset comprising seven severity stages, an approach not previously examined. By capitalizing on this unique resource, this study′s findings set a new benchmark for DR classification, highlighting the transformative potential of incorporating advanced data into AI models. This study developed a Vgg16 transfer learning model and gauged its performance against established algorithms including Vgg‐19, AlexNet, and SqueezeNet. Remarkably, our results achieved accuracy rates of 96.95, 96.75, 96.09, and 92.96, respectively, emphasizing the contribution of our work. We strongly emphasized comprehensive severity rating, yielding perfect and impressive F1‐scores of 1.00 for “mild NPDR” and 97.00 for “no DR signs.” The Vgg16‐TL model consistently outperformed other models across all severity levels, reinforcing the value of our discoveries. Our deep learning training process, carefully selecting a learning rate of 1e‐05, allowed continuous refinements in training and validation accuracy. Beyond metrics, our investigation underscores the vital clinical importance of precise DR classification for preventing vision loss. This study conclusively establishes deep learning as a powerful transformative tool for developing effective DR algorithms with the potential to improve patient outcomes and advance ophthalmology standards.

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Early diabetic eye damage: Comparing detection methods using diagnostic power

Cited by 5 publications

References 85 publications

Discriminating early-stage diabetic retinopathy with subjective and objective perimetry

Discriminating early-stage diabetic retinopathy with subjective and objective perimetry

Visual Field Tests: A Narrative Review of Different Perimetric Methods

Multi‐Deep Learning Approach With Transfer Learning for 7‐Stages Diabetic Retinopathy Classification

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