Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Methods: Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). Results: The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. Conclusion: To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.

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Accuracy of artificial intelligence–assisted detection of esophageal cancer and neoplasms on endoscopic images: a systematic review and meta‐analysis

Journal of Digestive Diseases ◽

10.1111/1751-2980.12992 ◽

2021 ◽

Author(s):

Si Min Zhang ◽

Yong Jun Wang ◽

Shu Tian Zhang

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Esophageal Cancer ◽

Meta Analysis ◽

Endoscopic Images

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Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽

10.1136/bmjopen-2020-043665 ◽

2021 ◽

Vol 11 (3) ◽

pp. e043665

Author(s):

Srinivasa Rao Kundeti ◽

Manikanda Krishnan Vaidyanathan ◽

Bharath Shivashankar ◽

Sankar Prasad Gorthi

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Diagnostic Accuracy ◽

Ischaemic Stroke ◽

Study Protocol ◽

Systematic Reviews ◽

Acute Ischaemic Stroke ◽

Meta Analysis ◽

Large Vessel ◽

Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

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1578. Treatments for complicated urinary tract infections (cUTI) caused by multidrug resistant (MDR) Gram-negative (GN) pathogens- a systematic review and network meta-analysis (NMA)

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1758 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S787-S787

Author(s):

Tim Reason ◽

Karan Gill ◽

Christopher Longshaw ◽

Rachael McCool ◽

Katy Wilson ◽

...

Keyword(s):

Systematic Review ◽

Health Economics ◽

Meta Analysis ◽

Treatment Options ◽

Final Analysis ◽

Similar Efficacy ◽

Global Public Health ◽

Efficacy And Safety ◽

Support Unit ◽

Tract Infections

Abstract Background Antimicrobial resistance is a major and growing threat to global public health. Cefiderocol (CFDC) is a new siderophore-cephalosporin with a wide activity spectrum covering all aerobic GN pathogens including all WHO critical priority pathogens, that was recently approved by FDA for the treatment of GN cUTI in susceptible organisms. We aim to understand the relative efficacy and safety of current treatment options for cUTI caused by MDR GN pathogens. Methods We conducted a systematic review to identify all relevant trials that investigated the efficacy and safety of antimicrobial regimens, for the treatment of GN pathogens in cUTI. Outcomes of interest included clinical cure and microbiological eradication (ME) at time of cure (TOC) and sustained follow up (SFU), and safety. Evidence networks were constructed using data for outcomes of interest and analyses were conducted in a frequentist framework using NMA methods outlined by the NICE decision support unit using the netmeta package in R. Results A total of 5 studies, 6 interventions and 2,349 randomised patients were included in the final analysis. Interventions included CFDC, imipenem-cilastatin (IPM-CIL), ceftazidime-avibactam (CAZ/AVI), doripenem (DOR), levofloxacin and ceftolozane-tazobactam (CEF/TAZ). Trials included predominantly Enterobacterales, and Pseudomonas aeruginosa and very few Acinetobacter baumannii. The patient population presented some clinical differences across trials, which were not adjusted for the NMA. Overall, there were numerical differences (especially in endpoints at SFU favouring CFDC), but all treatments showed similar efficacy and safety, with exception of higher ME rate at TOC for CFDC vs IPM, Table 1, also observed at SFU, consistent with the data from the individual clinical trial. Table 1- Results for microbiological eradication Table 1- Results for microbiological eradication Conclusion This NMA, showed superiority of CFDC vs IPM-CIL in ME at TOC and SFU and similar efficacy and safety vs all other comparators, with numeric differences favouring CFDC for outcomes at SFU. These traditional methodologies for NMA, are only valid within a similar pathogens pool and population across the trials, and may not reflect the full value of breadth of coverage that new therapeutic options bring for the treatment of MDR GN pathogens. Disclosures Tim Reason, PhD, Shionogi (Consultant) Karan Gill, MSc, Shionogi BV (Employee) Christopher Longshaw, PhD, Shionogi B.V. (Employee) Rachael McCool, PhD, York Health Economics Consortium (Employee, YHEC was commissioned by Shionogi to conduct the systematic review) Katy Wilson, PhD, York Health Economics Consortium (Employee, Shionogi commissioned YHEC to conduct the systematic review) Sara Lopes, PharmD, Shionogi BV (Employee)

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A Systematic Review and Meta-Analysis on Endoscopists’ Accuracy for Detecting Upper Gastrointestinal Neoplasia in Artificial Intelligence Studies: What is the Gold-Standard?

10.1055/s-0041-1724272 ◽

2021 ◽

Author(s):

L Frazzoni ◽

J Arribas ◽

G Antonelli ◽

C Hassan ◽

L Fuccio ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Gold Standard ◽

Meta Analysis ◽

Intelligence Studies ◽

Upper Gastrointestinal

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Influential Factors for Older Users’ Acceptance of eHealth Services: an Integrated Acceptance Framework (IAF) (Preprint)

10.2196/preprints.31920 ◽

2021 ◽

Author(s):

Jiyeon Yu ◽

Angelica de Antonio ◽

Elena Villalba-Mora

Keyword(s):

Systematic Review ◽

Older Adults ◽

New Technologies ◽

Meta Analysis ◽

Health Condition ◽

Influential Factors ◽

Current Status ◽

Five Dimensions ◽

The Impact ◽

Acceptance Factors

BACKGROUND eHealth and Telehealth play a crucial role in assisting older adults who visit hospitals frequently or who live in nursing homes and can benefit from staying at home while being cared for. Adapting to new technologies can be difficult for older people. Thus, to better apply these technologies to older adults’ lives, many studies have analyzed acceptance factors for this particular population. However, there is not yet a consensual framework to be used in further development and the search for solutions. OBJECTIVE This paper presents an Integrated Acceptance Framework (IAF) for the older user’s acceptance of eHealth, based on 43 studies selected through a systematic review. METHODS We conducted a four-step study. First, through a systematic review from 2010 to 2020 in the field of eHealth, the acceptance factors and basic data for analysis were extracted. Second, we carried out a thematic analysis to group the factors into themes to propose and integrated framework for acceptance. Third, we defined a metric to evaluate the impact of the factors addressed in the studies. Last, the differences amongst the important IAF factors were analyzed, according to the participants’ health conditions, verification time, and year. RESULTS Through the systematic review, 731 studies were founded in 5 major databases, resulting in 43 selected studies using the PRISMA methodology. First, the research methods and the acceptance factors for eHealth were compared and analyzed, extracting a total of 105 acceptance factors, which were grouped later, resulting in the Integrated Acceptance Framework. Five dimensions (i.e., personal, user-technology relational, technological, service-related, environmental) emerged with a total of 23 factors. Also, we assessed the quality of the evidence. And then, we conducted a stratification analysis to reveal the more appropriate factors depending on the health condition and the assessment time. Finally, we assess which are the factors and dimensions that are recently becoming more important. CONCLUSIONS The result of this investigation is a framework for conducting research on eHealth acceptance. To elaborately analyze the impact of the factors of the proposed framework, the criteria for evaluating the evidence from the studies that have extracted factors are presented. Through this process, the impact of each factor in the IAF has been presented, in addition to the framework proposal. Moreover, a meta-analysis of the current status of research is presented, highlighting the areas where specific measures are needed to facilitate e-Health acceptance.

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