Diabetische Retinopathie und Makulopathie

Hammes, Hans‐Peter; Lemmen, Klaus Dieter; Bertram, Bernd

doi:10.1007/s11428-021-00724-0

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…According to the national guideline ("Nationale Versorgungsleitlinie Typ-2-Diabetes: Therapie"), the patient with DM is responsible to maintain regular ophthalmological check-ups, while in the absence of a diagnosed retinopathy, the PC provider or diabetologist is responsible [ 7 ]. In contrast to the importance of regular screenings, approximately 20–40% of these patients do not meet the recommended screening intervals [ 8 ], with only an estimated 45–50% of eye exams indicated for patients with DM actually taking place [ 9 ]. Part of this problem is a lack of adherence to regular eye screenings which may potentially be improved by using information technology tools.…”

Section: Introductionmentioning

confidence: 99%

“…Such tools might improve the accessibility of health care and enable physicians to offer individualized screenings that are tailored to the risk of developing a retinopathy. This in turn could improve the willingness of patients with DM to participate in such screenings, as DM management is primarily accomplished by PC providers [ 8 ]. In addition, 90% of German citizens are in contact with their PC provider at least once a year [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care—A meta-analysis

Wewetzer¹,

Held²,

Steinhäuser³

2021

PLoS ONE

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Background Diabetic retinopathy (DR) affects 10–24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis. Purpose The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC. Data sources A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies. Study selection Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects. Data extraction The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures. Data synthesis and conclusion The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%. Limitations Selected studies showed a high variation in sample size and quality and quantity of available data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%