Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist

Salameh, Jean‐Paul; Bossuyt, Patrick; McGrath, Trevor A.; Thombs, Brett D.; Hyde, Christopher; Macaskill, Petra; Deeks, Jonathan J; Leeflang, Mariska; Korevaar, Daniël A.; Whiting, Penny; Takwoingi, Yemisi; Reitsma, Johannes B.; Cohen, Jérémie F.; Frank, Robert; Hunt, Harriet; Hooft, Lotty; Rutjes, Anne W S; Willis, Brian H; Gatsonis, Constantine; Levis, Brook; Moher, David; McInnes, Matthew D. F.

doi:10.1136/bmj.m2632

Cited by 343 publications

(265 citation statements)

References 99 publications

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“…Although we included images from subjects with cataracts (a leading cause of blindness in the world 21 ), dry eye, and strabismus (a common eye disorder that affects children 22 ), further research is warranted amongst different eye diseases, including corneal scar, corneal degeneration, pterygium, etc. In this study, we excluded 623 images from subjects with rare corneal diseases based on the PRISMA diagnostic test accuracy guideline 23 . How to detect eye laterality with rare corneal is still a challenge.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

Zheng

Xie

Wang

et al. 2021

Sci Rep

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This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer learning and fine-tuning of pre-trained deep convolutional neural networks (InceptionV3, VGG16, MobileNetV2) to develop DL models for determining the eye laterality. Testing datasets, from Scheimpflug and slit-lamp digital camera photography, were employed to test the DL model, and the results were compared with a classification performed by human experts. The performance of the DL model was evaluated by accuracy, sensitivity, specificity, operating characteristic curves, and corresponding area under the curve values. A total of 14,468 photographs were collected for the development of DL models. After training for 100 epochs, the DL models of the InceptionV3 mode achieved the area under the receiver operating characteristic curve of 0.998 (with 95% CI 0.924–0.958) for detecting eye laterality. In the external testing dataset (76 primary gaze photographs taken by a digital camera), the DL model achieves an accuracy of 96.1% (95% CI 91.7%–100%), which is better than an accuracy of 72.3% (95% CI 62.2%–82.4%), 82.8% (95% CI 78.7%–86.9%) and 86.8% (95% CI 82.5%–91.1%) achieved by human graders. Our study demonstrated that this high-performing DL model can be used for automated labeling for the laterality of eyes. Our DL model is useful for managing a large volume of the anterior segment images with a slit-lamp camera in the clinical setting.

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Section: Discussionmentioning

confidence: 99%

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

Zheng

Xie

Wang

et al. 2021

Sci Rep

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“…This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [18], its extension for diagnostic test accuracy [19,20], and the Cochrane Handbook for Systematic Reviews [21].…”

Section: Methodsmentioning

confidence: 99%

The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

Hornung

Steeb

Wessely

et al. 2021

IJERPH

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Early detection of melanoma is critical to reduce the mortality and morbidity rates of this tumor. Total body photography (TBP) may aid in the early detection of melanoma. To summarize the current evidence on TBP for the early detection of melanoma, we performed a systematic literature search in Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) for eligible records up to 6th August 2020. Outcomes of interest included melanoma incidence, incisional and excisional biopsy rates, as well as the Breslow’s index of detected tumors. Results from individual studies were described qualitatively. The risks of bias and applicability of the included studies was assessed using the QUADAS-2 checklist. In total, 14 studies published between 1997 and 2020 with an overall sample size of n = 12082 (range 100–4692) were included in the qualitative analysis. Individuals undergoing TBP showed a trend towards a lower Breslow’s thickness and a higher proportion of in situ melanomas compared to those without TBP. The number needed to excise one melanoma varied from 3:1 to 14.3:1 and was better for lesions that arose de novo than for tracked ones. The included studies were judged to be of unclear methodological concern with specific deficiencies in the domains “flow and timing” and “reference standard”. The use of TBP can improve the early detection of melanoma in high-risk populations. Future studies are warranted to reduce the heterogeneity of phenotypic risk factor definition and the technical implementation of TBP. Artificial intelligence-assisted analysis of images derived from 3-D TBP systems and digital dermoscopy may further improve the early detection of melanoma.

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“…This review of scientific literature followed the methodological guidelines contained in the PRISMA statement (14) for Diagnostic Accuracy Test (DTA) (PROSPERO protocol ID 184123 (15)). A systematic search of the available literature was performed using PubMed, Scopus, and Google scholar databases (time period included 2010-September 2020).…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

et al. 2021

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Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8–93.2%) and specificity (85.2%, 95%CI 84.9–85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3–93.1%; specificity 65.2%, 55–74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.

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Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist

Abstract: PRISMA-DTA item 1: title Identify the report as a systematic review (with or without meta-analysis) of DTA studies. Examples 1. Diagnostic accuracy of saline contrast sonohysterography in detecting endometrial For numbered affiliations see end of the article.

Cited by 343 publications

References 99 publications

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

The Value of Total Body Photography for the Early Detection of Melanoma: A Systematic Review

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

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