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  1. Article ; Online: Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

    Chuter, Benton / Huynh, Justin / Bowd, Christopher / Walker, Evan / Rezapour, Jasmin / Brye, Nicole / Belghith, Akram / Fazio, Massimo A / Girkin, Christopher A / De Moraes, Gustavo / Liebmann, Jeffrey M / Weinreb, Robert N / Zangwill, Linda M / Christopher, Mark

    Translational vision science & technology

    2024  Volume 13, Issue 1, Page(s) 23

    Abstract: Purpose: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.: Methods: Image quality ground truth was determined ... ...

    Abstract Purpose: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.
    Methods: Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC).
    Results: The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88).
    Conclusions: The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model.
    Translational relevance: Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.
    MeSH term(s) Humans ; Deep Learning ; Glaucoma, Open-Angle/diagnosis ; Diagnostic Techniques, Ophthalmological ; Glaucoma ; Ocular Hypertension ; Fundus Oculi
    Language English
    Publishing date 2024-01-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2674602-5
    ISSN 2164-2591 ; 2164-2591
    ISSN (online) 2164-2591
    ISSN 2164-2591
    DOI 10.1167/tvst.13.1.23
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  2. Article ; Online: One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis.

    Fan, Rui / Bowd, Christopher / Brye, Nicole / Christopher, Mark / Weinreb, Robert N / Kriegman, David J / Zangwill, Linda M

    IEEE transactions on medical imaging

    2023  Volume 42, Issue 12, Page(s) 3764–3778

    Abstract: Convolutional neural networks (CNNs) are a promising technique for automated glaucoma diagnosis from images of the fundus, and these images are routinely acquired as part of an ophthalmic exam. Nevertheless, CNNs typically require a large amount of well- ... ...

    Abstract Convolutional neural networks (CNNs) are a promising technique for automated glaucoma diagnosis from images of the fundus, and these images are routinely acquired as part of an ophthalmic exam. Nevertheless, CNNs typically require a large amount of well-labeled data for training, which may not be available in many biomedical image classification applications, especially when diseases are rare and where labeling by experts is costly. This article makes two contributions to address this issue: 1) It extends the conventional Siamese network and introduces a training method for low-shot learning when labeled data are limited and imbalanced, and 2) it introduces a novel semi-supervised learning strategy that uses additional unlabeled training data to achieve greater accuracy. Our proposed multi-task Siamese network (MTSN) can employ any backbone CNN, and we demonstrate with four backbone CNNs that its accuracy with limited training data approaches the accuracy of backbone CNNs trained with a dataset that is 50 times larger. We also introduce One-Vote Veto (OVV) self-training, a semi-supervised learning strategy that is designed specifically for MTSNs. By taking both self-predictions and contrastive predictions of the unlabeled training data into account, OVV self-training provides additional pseudo labels for fine-tuning a pre-trained MTSN. Using a large (imbalanced) dataset with 66,715 fundus photographs acquired over 15 years, extensive experimental results demonstrate the effectiveness of low-shot learning with MTSN and semi-supervised learning with OVV self-training. Three additional, smaller clinical datasets of fundus images acquired under different conditions (cameras, instruments, locations, populations) are used to demonstrate the generalizability of the proposed methods.
    MeSH term(s) Humans ; Glaucoma/diagnostic imaging ; Fundus Oculi ; Neural Networks, Computer ; Supervised Machine Learning
    Language English
    Publishing date 2023-11-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2023.3307689
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  3. Article ; Online: Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

    Bowd, Christopher / Belghith, Akram / Rezapour, Jasmin / Christopher, Mark / Jonas, Jost B / Hyman, Leslie / Fazio, Massimo A / Weinreb, Robert N / Zangwill, Linda M

    Journal of glaucoma

    2023  Volume 32, Issue 10, Page(s) 841–847

    Abstract: Prcis: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma ... ...

    Abstract Prcis: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia.
    Background/aims: To evaluate the diagnostic accuracy of a multimodal DL classifier using wide OCT optic nerve head cube scans in eyes with and without axial high myopia.
    Materials and methods: Three hundred seventy-one primary open angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia [axial length (AL) ≤ 26 mm] and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL > 26 mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: (1) texture-based en face image, (2) retinal nerve fiber layer (RNFL) thickness map image, and (3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves were used to compare model accuracy.
    Results: Adjusted area under the receiver operating curve for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models [0.83 (0.79, 0.86) for texture-based en face image; 0.84 (0.81, 0.87) for RNFL thickness map; and 0.68 (0.61, 0.74) for cSLO image; all P ≤ 0.05]. Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy [0.89 (0.86, 0.92)] compared with texture en face image [0.83 (0.78, 0.85)], RNFL [0.85 (0.81, 0.86)] and cSLO image models [0.69 (0.63, 0.76)] (all P ≤ 0.05).
    Conclusions: Combining OCT-based RNFL thickness maps with texture-based en face images showed a better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.
    MeSH term(s) Humans ; Optic Disk ; Glaucoma, Open-Angle/diagnosis ; Deep Learning ; Intraocular Pressure ; Retinal Ganglion Cells ; Myopia/diagnosis ; Tomography, Optical Coherence/methods
    Language English
    Publishing date 2023-07-19
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 913494-3
    ISSN 1536-481X ; 1057-0829
    ISSN (online) 1536-481X
    ISSN 1057-0829
    DOI 10.1097/IJG.0000000000002267
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  4. Article ; Online: Central visual field damage in glaucoma eyes with choroidal microvasculature dropout with and without high axial myopia.

    Micheletti, Eleonora / El-Nimri, Nevin / Nishida, Takashi / Moghimi, Sasan / Rezapour, Jasmin / Fazio, Massimo A / Suh, Min Hee / Bowd, Christopher / Belghith, Akram / Christopher, Mark / Jonas, Jost B / Weinreb, Robert N / Zangwill, Linda M

    The British journal of ophthalmology

    2024  Volume 108, Issue 3, Page(s) 372–379

    Abstract: Purpose: To characterise the relationship between a deep-layer microvasculature dropout (MvD) and central visual field (VF) damage in primary open-angle glaucoma (POAG) patients with and without high axial myopia.: Design: Cross-sectional study.: ... ...

    Abstract Purpose: To characterise the relationship between a deep-layer microvasculature dropout (MvD) and central visual field (VF) damage in primary open-angle glaucoma (POAG) patients with and without high axial myopia.
    Design: Cross-sectional study.
    Methods: Seventy-one eyes (49 patients) with high axial myopia and POAG and 125 non-highly myopic POAG eyes (97 patients) were enrolled. Presence, area and angular circumference of juxtapapillary MvD were evaluated on optical coherence tomography angiography B-scans and en-face choroidal images.
    Results: Juxtapapillary MvD was detected more often in the highly myopic POAG eyes (43 eyes, 86%) than in the non-highly myopic eyes (73 eyes, 61.9%; p=0.002). In eyes with MvD, MvD area and angular circumference (95% CI) were significantly larger in the highly myopic eyes compared with the non-highly myopic eyes (area: (0.69 (0.40, 0.98) mm
    Conclusions: MvD was more prevalent and larger in POAG eyes with high myopia than in non-highly myopic POAG eyes. In both groups, eyes with MvD showed worse glaucoma severity and more central VF defects.
    MeSH term(s) Humans ; Visual Fields ; Glaucoma, Open-Angle/diagnosis ; Glaucoma, Open-Angle/complications ; Cross-Sectional Studies ; Intraocular Pressure ; Glaucoma/complications ; Myopia/complications ; Myopia/diagnosis ; Tomography, Optical Coherence/methods ; Scotoma ; Microvessels
    Language English
    Publishing date 2024-02-21
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 80078-8
    ISSN 1468-2079 ; 0007-1161
    ISSN (online) 1468-2079
    ISSN 0007-1161
    DOI 10.1136/bjo-2022-322234
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  5. Article: Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

    Christopher, Mark / Gonzalez, Ruben / Huynh, Justin / Walker, Evan / Radha Saseendrakumar, Bharanidharan / Bowd, Christopher / Belghith, Akram / Goldbaum, Michael H / Fazio, Massimo A / Girkin, Christopher A / De Moraes, Carlos Gustavo / Liebmann, Jeffrey M / Weinreb, Robert N / Baxter, Sally L / Zangwill, Linda M

    Bioengineering (Basel, Switzerland)

    2024  Volume 11, Issue 2

    Abstract: A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting ... ...

    Abstract A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.
    Language English
    Publishing date 2024-01-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2746191-9
    ISSN 2306-5354
    ISSN 2306-5354
    DOI 10.3390/bioengineering11020140
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  6. Article ; Online: Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.

    Bowd, Christopher / Belghith, Akram / Rezapour, Jasmin / Christopher, Mark / Hyman, Leslie / Jonas, Jost B / Weinreb, Robert N / Zangwill, Linda M

    American journal of ophthalmology

    2022  Volume 242, Page(s) 26–35

    Abstract: Purpose: To evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to ... ...

    Abstract Purpose: To evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare SALSA-Texture with standard macular ganglion cell-inner plexiform layer (GCIPL) thickness, macular retinal nerve fiber layer (mRNFL) thickness, and ganglion cell complex (GCC) thickness maps.
    Design: Comparison of diagnostic approaches.
    Methods: Cross-sectional data were collected from 92 eyes with primary open-angle glaucoma (POAG) and 44 healthy control eyes with axial high myopia (axial length >26 mm). Optical coherence tomography texture en face images, developed using SALSA-Texture to model the spatial arrangement patterns of the pixel intensities in a region, were generated from 70-μm slabs just below the vitreal border of the inner limiting membrane. Areas under the receiver operating characteristic curves (AUROCs) and areas under the precision recall curves (AUPRCs) adjusted for both eyes, axial length, age, disc area, and image quality were used to compare different approaches.
    Results: The best parameter-adjusted AUROCs (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes were 0.92 (0.88-0.94) for texture en face images, 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness. A subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes) showed the best AUROC was 0.92 (0.89-0.94) for texture en face images compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness (P ≤ .02 compared with texture for all comparisons).
    Conclusion: The current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes. While further investigation is needed, texture en face images show promise for improving the detection of glaucoma in eyes with high myopia where traditional retinal layer segmentation often is challenging.
    MeSH term(s) Cross-Sectional Studies ; Glaucoma/diagnosis ; Glaucoma, Open-Angle/diagnosis ; Humans ; Intraocular Pressure ; Myopia/complications ; Myopia/diagnosis ; ROC Curve ; Retinal Ganglion Cells ; Tomography, Optical Coherence/methods
    Language English
    Publishing date 2022-05-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 80030-2
    ISSN 1879-1891 ; 0002-9394
    ISSN (online) 1879-1891
    ISSN 0002-9394
    DOI 10.1016/j.ajo.2022.04.019
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  7. Article ; Online: Reply.

    Christopher, Mark / Bowd, Christopher / Belghith, Akram / Goldbaum, Michael H / Weinreb, Robert N / Fazio, Massimo A / Girkin, Christopher A / Liebmann, Jeffrey M / Zangwill, Linda M

    Ophthalmology

    2021  Volume 129, Issue 1, Page(s) e5

    Language English
    Publishing date 2021-10-07
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 392083-5
    ISSN 1549-4713 ; 0161-6420
    ISSN (online) 1549-4713
    ISSN 0161-6420
    DOI 10.1016/j.ophtha.2021.08.022
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  8. Article ; Online: Racial Differences in Diagnostic Accuracy of Retinal Nerve Fiber Layer Thickness in Primary Open-Angle Glaucoma.

    KhalafAllah, Mahmoud T / Zangwill, Linda M / Proudfoot, James / Walker, Evan / Girkin, Christopher A / Fazio, Massimo A / Weinreb, Robert N / Bowd, Christopher / Moghimi, Sasan / De Moraes, C Gustavo / Liebmann, Jeffrey M / Racette, Lyne

    American journal of ophthalmology

    2023  Volume 259, Page(s) 7–14

    Abstract: Purpose: To evaluate the diagnostic accuracy of retinal nerve fiber layer thickness (RNFLT) by spectral-domain optical coherence tomography (OCT) in primary open-angle glaucoma (POAG) in eyes of African (AD) and European descent (ED).: Design: ... ...

    Abstract Purpose: To evaluate the diagnostic accuracy of retinal nerve fiber layer thickness (RNFLT) by spectral-domain optical coherence tomography (OCT) in primary open-angle glaucoma (POAG) in eyes of African (AD) and European descent (ED).
    Design: Comparative diagnostic accuracy analysis by race.
    Participants: 379 healthy eyes (125 AD and 254 ED) and 442 glaucomatous eyes (226 AD and 216 ED) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study.
    Methods: Spectralis (Heidelberg Engineering GmbH) and Cirrus (Carl Zeiss Meditec) OCT scans were taken within one year from each other.
    Main outcome measures: Diagnostic accuracy of RNFLT measurements.
    Results: Diagnostic accuracy for Spectralis-RNFLT was significantly lower in eyes of AD compared to those of ED (area under the receiver operating curve [AUROC]: 0.85 and 0.91, respectively,
    Conclusions: OCT-RNFLT has lower diagnostic accuracy in eyes of AD compared to those of ED. This finding was generally robust across two OCT instruments and remained after adjustment for many potential confounders. Further studies are needed to explore the potential sources of this difference.
    MeSH term(s) Humans ; Glaucoma, Open-Angle/ethnology ; Glaucoma, Open-Angle/diagnosis ; Tomography, Optical Coherence/methods ; Nerve Fibers/pathology ; Retinal Ganglion Cells/pathology ; Female ; Male ; Middle Aged ; Intraocular Pressure/physiology ; ROC Curve ; Visual Fields/physiology ; White People/ethnology ; Reproducibility of Results ; Aged ; Optic Disk/pathology ; Optic Disk/diagnostic imaging ; Optic Nerve Diseases/diagnosis ; Optic Nerve Diseases/ethnology ; Black or African American/ethnology ; Area Under Curve ; Sensitivity and Specificity
    Language English
    Publishing date 2023-10-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Comparative Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 80030-2
    ISSN 1879-1891 ; 0002-9394
    ISSN (online) 1879-1891
    ISSN 0002-9394
    DOI 10.1016/j.ajo.2023.10.012
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  9. Article ; Online: Agreement Between 10-2 and 24-2C Visual Field Test Protocols for Detecting Glaucomatous Central Visual Field Defects.

    Chakravarti, Tutul / Moghadam, Mohamad / Proudfoot, James A / Weinreb, Robert N / Bowd, Christopher / Zangwill, Linda M

    Journal of glaucoma

    2021  Volume 30, Issue 6, Page(s) e285–e291

    Abstract: Precis: Moderate to substantial agreement between 10-2 and 24-2C perimetry for detecting central field defects suggests that adding central test points to the 24-2 protocol may improve efficiency of visual field (VF) testing for glaucoma management.: ... ...

    Abstract Precis: Moderate to substantial agreement between 10-2 and 24-2C perimetry for detecting central field defects suggests that adding central test points to the 24-2 protocol may improve efficiency of visual field (VF) testing for glaucoma management.
    Purpose: The purpose of this study was to assess agreement between Humphrey Visual Field Analyzer 10-2 and 24-2C test protocols for detecting glaucomatous defects in the central 10 degrees of the visual field (CVFDs).
    Materials and methods: VFs from 165 eyes of 18 healthy individuals, 12 glaucoma suspects and 62 glaucoma patients who completed 10-2 and 24-2C VF testing protocols within 6 months were included. CVFDs on 10-2 and 24-2C (within the central 22 points) test grids required a cluster of 3 contiguous points with P<5%, 5%, and 1% or <5%, 2%, and 2% within a hemifield on the total deviation (TD) or pattern deviation (PD) plot. Cohen kappa (k) was used to assess agreement between 10-2 and 24-2C test grids in identifying CVFDs. Specificity of each testing strategy was assessed in VFs from healthy eyes.
    Results: CVFDs in suspect and glaucoma eyes were combined and reported as localized to superior, inferior or both hemifields based on TD and PD plots for 10-2 and 24-2C test grids. Moderate to substantial agreement was observed between 10-2 and 24-2C grids for detecting any CVFD from PD (k=0.551) and TD (k=0.651) plots. Specificity was high in healthy eyes ranging from 0.94 to 1.0 for both test protocols.
    Conclusion: Substantial agreement for identifying CVFDs using the 24-2C and 10-2 protocols suggests that combining tests by adding central test points to the 24-2 test grid may supplant the need for 2 perimetry regimens for detecting central and peripheral glaucomatous VF damage.
    MeSH term(s) Glaucoma/diagnosis ; Humans ; Intraocular Pressure ; Vision Disorders/diagnosis ; Visual Field Tests ; Visual Fields
    Language English
    Publishing date 2021-03-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 913494-3
    ISSN 1536-481X ; 1057-0829
    ISSN (online) 1536-481X
    ISSN 1057-0829
    DOI 10.1097/IJG.0000000000001844
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  10. Article: Optical coherence tomography for clinical detection and monitoring of glaucoma?

    Bowd, Christopher

    The British journal of ophthalmology

    2007  Volume 91, Issue 7, Page(s) 853–854

    MeSH term(s) Databases, Factual ; Glaucoma/diagnosis ; Glaucoma/therapy ; Humans ; Reference Values ; Tomography, Optical Coherence
    Language English
    Publishing date 2007-07
    Publishing country England
    Document type Comment ; Editorial
    ZDB-ID 80078-8
    ISSN 1468-2079 ; 0007-1161
    ISSN (online) 1468-2079
    ISSN 0007-1161
    DOI 10.1136/bjo.2006.113100
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