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  1. Article ; Online: Correspondence.

    Haj Najeeb, Bilal / Gerendas, Bianca S / Schmidt-Erfurth, Ursula

    Retina (Philadelphia, Pa.)

    2023  Volume 44, Issue 3, Page(s) e20

    Language English
    Publishing date 2023-10-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 603192-4
    ISSN 1539-2864 ; 0275-004X
    ISSN (online) 1539-2864
    ISSN 0275-004X
    DOI 10.1097/IAE.0000000000003955
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  2. Book ; Thesis: Prospektive Evaluation der gesundheitsbezogenen Lebensqualität bei Patienten mit chronischer Otitis media

    Gerendas, Bianca S.

    2010  

    Author's details vorgelegt von Bianca S. Gerendas
    Language German
    Size 109 S.
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Heidelberg, Univ., Diss., 2010
    HBZ-ID HT016703346
    Database Catalogue ZB MED Medicine, Health

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  3. Article ; Online: Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

    Pawloff, Maximilian / Gerendas, Bianca S / Deak, Gabor / Bogunovic, Hrvoje / Gruber, Anastasiia / Schmidt-Erfurth, Ursula

    Eye (London, England)

    2023  Volume 37, Issue 18, Page(s) 3793–3800

    Abstract: Purpose: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated ... ...

    Abstract Purpose: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values.
    Methods: We utilized an automated deep learning approach to quantify macular fluid in SD-OCT volumes (Cirrus, Spectralis, Topcon) from patients of HAWK and HARRIER Studies. Three-dimensional volumes for IRF and SRF were measured at baseline and under therapy in the central millimeter and compared to fluid gradings, CSFT and foveal centerpoint thickness (CPT) values measured by the Vienna Reading Center.
    Results: 41.906 SD-OCT volume scans were included into the analysis. Concordance between human expert grading and automated algorithm performance reached AUC values of 0.93/0.85 for IRF and 0.87 for SRF in HARRIER/HAWK in the central millimeter. IRF volumes showed a moderate correlation with CSFT at baseline (HAWK: r = 0.54; HARRIER: r = 0.62) and weaker correlation under therapy (HAWK: r = 0.44; HARRIER: r = 0.34). SRF and CSFT correlations were low at baseline (HAWK: r = 0.29; HARRIER: r = 0.22) and under therapy (HAWK: r = 0.38; HARRIER: r = 0.45). The residual standard error (IRF: 75.90 µm; SRF: 95.26 µm) and marginal residual standard deviations (IRF: 46.35 µm; SRF: 44.19 µm) of fluid volume were high compared to the range of CSFT values.
    Conclusion: Deep learning-based segmentation of retinal fluid performs reliably on OCT images. CSFT values are weak indicators for fluid activity in nAMD. Automated quantification of fluid types, highlight the potential of deep learning-based approaches to objectively monitor anti-VEGF therapy.
    MeSH term(s) Humans ; Angiogenesis Inhibitors/therapeutic use ; Tomography, Optical Coherence/methods ; Deep Learning ; Reproducibility of Results ; Vascular Endothelial Growth Factor A ; Visual Acuity ; Wet Macular Degeneration/diagnosis ; Wet Macular Degeneration/drug therapy ; Intravitreal Injections ; Subretinal Fluid/diagnostic imaging
    Chemical Substances Angiogenesis Inhibitors ; Vascular Endothelial Growth Factor A
    Language English
    Publishing date 2023-06-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 91001-6
    ISSN 1476-5454 ; 0950-222X
    ISSN (online) 1476-5454
    ISSN 0950-222X
    DOI 10.1038/s41433-023-02615-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Deep Learning-Based Automated Optical Coherence Tomography Segmentation in Clinical Routine: Getting Closer.

    Gerendas, Bianca S / Bogunovic, Hrvoje / Schmidt-Erfurth, Ursula

    JAMA ophthalmology

    2021  Volume 139, Issue 9, Page(s) 973–974

    MeSH term(s) Deep Learning ; Humans ; Tomography, Optical Coherence/methods
    Language English
    Publishing date 2021-07-08
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2701705-9
    ISSN 2168-6173 ; 2168-6165
    ISSN (online) 2168-6173
    ISSN 2168-6165
    DOI 10.1001/jamaophthalmol.2021.2309
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  5. Article ; Online: An Automated Comparative Analysis of the Exudative Biomarkers in Neovascular Age-Related Macular Degeneration, The RAP Study: Report 6.

    Haj Najeeb, Bilal / Gerendas, Bianca S / Deak, Gabor G / Leingang, Oliver / Bogunovic, Hrvoje / Schmidt-Erfurth, Ursula

    American journal of ophthalmology

    2024  Volume 264, Page(s) 53–65

    Abstract: Purpose: To investigate differences in volume and distribution of the main exudative biomarkers across all types and subtypes of macular neovascularization (MNV) using artificial intelligence (AI).: Design: Cross-sectional study.: Methods: An AI- ... ...

    Abstract Purpose: To investigate differences in volume and distribution of the main exudative biomarkers across all types and subtypes of macular neovascularization (MNV) using artificial intelligence (AI).
    Design: Cross-sectional study.
    Methods: An AI-based analysis was conducted on 34,528 OCT B-scans consisting of 281 (250 unifocal, 31 multifocal) MNV3, 55 MNV2, and 121 (30 polypoidal, 91 non-polypoidal) MNV1 treatment-naive eyes. Means (SDs), medians and heat maps of cystic intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachments (PED), and hyperreflective foci (HRF) volumes, as well as retinal thickness (RT) were compared among MNV types and subtypes.
    Results: MNV3 had the highest mean IRF with 291 (290) nL, RT with 357 (49) µm, and HRF with 80 (70) nL, P ≤ .05. MNV1 showed the greatest mean SRF with 492 (586) nL, whereas MNV3 exhibited the lowest with 218 (382) nL, P ≤ .05. Heat maps showed IRF confined to the center, whereas SRF was scattered in all types. SRF, HRF, and PED were more distributed in the temporal macular half in MNV3. Means of IRF, HRF, and PED were higher in the multifocal than in the unifocal MNV3 with 416 (309) nL,114 (95) nL, and 810 (850) nL, P ≤ .05. Compared to the non-polypoidal subtype, the polypoidal subtype had greater means of SRF with 695 (718) nL, HRF 69 (63) nL, RT 357 (45) µm, and PED 1115 (1170) nL, P ≤ .05.
    Conclusions: This novel quantitative AI analysis shows that SRF is a biomarker of choroidal origin in MNV1, whereas IRF, HRF, and RT are retinal biomarkers in MNV3. Polypoidal MNV1 and multifocal MNV3 present with higher exudation compared to other subtypes.
    Language English
    Publishing date 2024-02-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80030-2
    ISSN 1879-1891 ; 0002-9394
    ISSN (online) 1879-1891
    ISSN 0002-9394
    DOI 10.1016/j.ajo.2024.02.018
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  6. Article ; Online: Association of microvascular biomarkers in fluorescein angiography with macrovascular-related mortality in clinical routine data.

    Goldbach, Felix / Mylonas, Georgios / Riegelnegg, Martin / Brugger, Jonas / Schmidt-Erfurth, Ursula / Gerendas, Bianca S

    PloS one

    2022  Volume 17, Issue 5, Page(s) e0266423

    Abstract: Purpose: Early detection of microvascular changes in the retina may be important for the risk assessment of cardiovascular health. Therefore, the purpose of this study was to investigate imaging biomarkers in fluorescein angiography (FA) as potential ... ...

    Abstract Purpose: Early detection of microvascular changes in the retina may be important for the risk assessment of cardiovascular health. Therefore, the purpose of this study was to investigate imaging biomarkers in fluorescein angiography (FA) as potential predictors for cardiovascular mortality.
    Methods: In this retrospective, matched case-control study, we included FA images from clinical routine data between 2007 and 2018 of 100 patients who died of macrovascular events (Group 1) and 100 age- and sex-matched controls (Group 2). All patients were under treatment for different, mostly retinal, ocular diseases. FA images were used for the measurement of the foveal avascular zone (FAZ) and the arteriolar and venular caliber.
    Results: Patients mean age on examination day was 69.5 ± 8.3 years with a 1:1 female:male subject ratio. Mean FAZ area of our sample was 0.340 ± 0.135 mm2 for Group 1 and 0.264 ± 0.137 mm2 for Group 2 (P < 0.001), showing a larger FAZ area in patients who subsequently died of macrovascular-related systemic diseases.
    Conclusions: Individuals effected by a macrovascular-related disease show a larger FAZ on FA examinations before the event compared to patients which are unaffected. Our results highlight a possible role of the FAZ as additional biomarker for the cardiovascular condition.
    MeSH term(s) Biomarkers ; Case-Control Studies ; Female ; Fluorescein Angiography/methods ; Fovea Centralis/blood supply ; Humans ; Male ; Retinal Diseases ; Retinal Vessels/diagnostic imaging ; Retrospective Studies ; Tomography, Optical Coherence/methods
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-05-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0266423
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  7. Article: Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures.

    Hofer, Dominik / Schmidt-Erfurth, Ursula / Orlando, José Ignacio / Goldbach, Felix / Gerendas, Bianca S / Seeböck, Philipp

    Biomedical optics express

    2022  Volume 13, Issue 5, Page(s) 2566–2580

    Abstract: In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally ... ...

    Abstract In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.
    Language English
    Publishing date 2022-04-04
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2572216-5
    ISSN 2156-7085
    ISSN 2156-7085
    DOI 10.1364/BOE.452873
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  8. Article ; Online: Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

    Schurer-Waldheim, Simon / Seebock, Philipp / Bogunovic, Hrvoje / Gerendas, Bianca S / Schmidt-Erfurth, Ursula

    IEEE journal of biomedical and health informatics

    2022  Volume 26, Issue 8, Page(s) 3927–3937

    Abstract: The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes is ... ...

    Abstract The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes is important for assessing visual function correlates and treatment guidance in macular disease. In this study, the "PRE U-net" is introduced as a novel approach for a fully automated fovea centralis detection, addressing the localization as a pixel-wise regression task. 2D B-scans are sampled from each image volume and are concatenated with spatial location information to train the deep network. A total of 5586 OCT volumes from 1,541 eyes were used to train, validate and test the deep learning method. The test data is comprised of healthy subjects and patients affected by neovascular age-related macular degeneration (nAMD), diabetic macula edema (DME) and macular edema from retinal vein occlusion (RVO), covering the three major retinal diseases responsible for blindness. Our experiments demonstrate that the PRE U-net significantly outperforms state-of-the-art methods and improves the robustness of automated localization, which is of value for clinical practice.
    MeSH term(s) Deep Learning ; Diabetic Retinopathy ; Fovea Centralis/diagnostic imaging ; Humans ; Macular Edema/diagnostic imaging ; Retina/diagnostic imaging ; Retinal Diseases ; Tomography, Optical Coherence/methods
    Language English
    Publishing date 2022-08-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2022.3166068
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  9. Article ; Online: Correspondence.

    Haj Najeeb, Bilal / Deak, Gabor G / Gerendas, Bianca S / Schmidt-Erfurth, Ursula

    Retina (Philadelphia, Pa.)

    2022  Volume 42, Issue 3, Page(s) e18–e20

    Language English
    Publishing date 2022-10-10
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 603192-4
    ISSN 1539-2864 ; 0275-004X
    ISSN (online) 1539-2864
    ISSN 0275-004X
    DOI 10.1097/IAE.0000000000003365
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  10. Article ; Online: Reply.

    Mylonas, Georgios / Haj Najeeb, Bilal / Goldbach, Felix / Deak, Gabor G / Michl, Martin / Brugger, Jonas / Schmidt-Erfurth, Ursula / Gerendas, Bianca S

    Retina (Philadelphia, Pa.)

    2023  Volume 43, Issue 7, Page(s) e41–e42

    Language English
    Publishing date 2023-04-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 603192-4
    ISSN 1539-2864 ; 0275-004X
    ISSN (online) 1539-2864
    ISSN 0275-004X
    DOI 10.1097/IAE.0000000000003804
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