Meta-analysis and systematic review to determine the optimal imaging modality for the detection of bladder deep endometriosis

The aim of this paper is to summarize and evaluate results from existing studies on accuracy and clinical value of three-dimensional printed heart models (3DPHM) for determining whether 3D printing can significantly improve on how the congenital heart disease (CHD) is managed in current clinical practice. Proquest, Google Scholar, Scopus, PubMed, and Medline were searched for relevant studies until April 2019. Two independent reviewers performed manual data extraction and assessed the risk of bias of the studies using the tools published on National Institutes of Health (NIH) website. The following data were extracted from the studies: author, year of publication, study design, imaging modality, segmentation software, utility of 3DPHM, CHD types, and dimensional accuracy. R software was used for the meta-analysis. Twenty-four articles met the inclusion criteria and were included in the systematic review. However, only 7 studies met the statistical requirements and were eligible for meta-analysis. Cochran’s Q test demonstrated significant variation among the studies for both of the meta-analyses of accuracy of 3DPHM and the utility of 3DPHM in medical education. Analysis of all included studies reported the mean deviation between the 3DPHM and the medical images is not significant, implying that 3DPHM are highly accurate. As for the utility of the 3DPHM, it is reported in all relevant studies that the 3DPHM improve the learning experience and satisfaction among the users, and play a critical role in facilitating surgical planning of complex CHD cases. 3DPHM have the potential to enhance communication in medical practice, however their clinical value remains debatable. More studies are required to yield a more meaningful meta-analysis.

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Meta-Analysis and Systematic Review to Determine the Optimum Imaging Modality for the Detection of Uterosacral Ligament Deep Infiltrative Endometriosis

Journal of Minimally Invasive Gynecology ◽

10.1016/j.jmig.2016.08.580 ◽

2016 ◽

Vol 23 (7) ◽

pp. S166-S167

Author(s):

B Gerges ◽

B Nadim ◽

W Martins ◽

G Condous

Keyword(s):

Systematic Review ◽

Imaging Modality ◽

Meta Analysis ◽

Uterosacral Ligament ◽

Deep Infiltrative Endometriosis ◽

Optimum Imaging

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Mechanical Thrombectomy in Patients with a Large Ischemic Volume at Presentation: Systematic Review and Meta-Analysis

Journal of Stroke ◽

10.5853/jos.2021.00724 ◽

2021 ◽

Vol 23 (3) ◽

pp. 358-366

Author(s):

Basile Kerleroux ◽

Kevin Janot ◽

Jean François Hak ◽

Johannes Kaesmacher ◽

Wagih Ben Hassen ◽

...

Keyword(s):

Systematic Review ◽

Mechanical Thrombectomy ◽

Imaging Modality ◽

Meta Analysis ◽

Sensitivity Analyses ◽

Control Group ◽

Z Value ◽

Ischemic Core ◽

Meta Analyses ◽

Core Volume

The benefits of mechanical thrombectomy (MT) for patients with acute ischemic stroke (AIS) and a large ischemic core (LIC) at presentation are uncertain. We aimed to obtain up-to-date aggregate estimates of the outcomes following MT in patients with volumetrically assessed LIC. We conducted a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-conformed, PROSPERO-registered, systematic review and meta-analysis of studies that included patients with AIS and a baseline LIC treated with MT, reported ischemic core volume quantitatively, and included patients with a LIC defined as a core volume ≥50 mL. The search was restricted to studies published between January 2015 and June 2020. Random-effects-meta-analysis was used to assess the effect of MT on 90-day unfavorable outcome (i.e., modified Rankin Scale [mRS] 3–6), mortality, and symptomatic intracranial hemorrhage (sICH) occurrence. Sensitivity analyses were performed for imaging-modality (computed tomography-perfusion or magnetic resonance-diffusion weighted imaging) and LIC-definition (≥50 or ≥70 mL). We analyzed 10 studies (954 patients), including six (682 patients) with a control group, allowing to compare 332 patients with MT to 350 who received best-medical-management alone. Overall, after MT the rate of patients with mRS 3–6 at 90 days was 74% (99% confidence interval [CI], 67 to 84; Z-value=7.04; I2=92.3%) and the rate of 90-day mortality was 36% (99% CI, 33 to 40; Z-value=–7.07; I2=74.5). Receiving MT was associated with a significant decrease in mRS 3–6 odds ratio (OR) 0.19 (99% CI, 0.11 to 0.33; P<0.01; Z-value=–5.92; I2=62.56) and in mortality OR 0.60 (99% CI, 0.34 to 1.06; P=0.02; Z-value=–2.30; I2=58.72). Treatment group did not influence the proportion of patients experiencing sICH, OR 0.96 (99% CI, 0.2 to 1.49; P=0.54; Z-value=–0.63; I2=64.74). Neither imaging modality for core assessment, nor LIC definition influenced the aggregated outcomes. Using aggregate estimates, MT appeared to decrease the risk of unfavorable functional outcome in patients with a LIC assessed volumetrically at baseline.

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Systematic review and meta-analysis of training mode, imaging modality and body size influences on the morphology and function of the male athlete's heart

Heart ◽

10.1136/heartjnl-2012-303465 ◽

2013 ◽

Vol 99 (23) ◽

pp. 1727-1733 ◽

Cited By ~ 121

Author(s):

Victor Utomi ◽

David Oxborough ◽

Greg P Whyte ◽

John Somauroo ◽

Sanjay Sharma ◽

...

Keyword(s):

Systematic Review ◽

Body Size ◽

Imaging Modality ◽

Meta Analysis ◽

Athlete’S Heart ◽

Training Mode ◽

And Function ◽

Athlete's Heart

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Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Applied Sciences ◽

10.3390/app11020842 ◽

2021 ◽

Vol 11 (2) ◽

pp. 842

Author(s):

Carolina Magalhaes ◽

Joaquim Mendes ◽

Ricardo Vardasca

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Learning Strategies ◽

Imaging Modality ◽

Meta Analysis ◽

Skin Surface ◽

Therapeutic Assessment ◽

Qualitative Synthesis ◽

Physiological Processes ◽

Bibliographic Search

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.

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