scholarly journals The diagnostic accuracy of artificial intelligence and computer-aided diagnosis for the detection and characterisation of colorectal polyps: A systematic review and meta-analysis. (Preprint)

10.2196/27370 ◽  
2021 ◽  
Author(s):  
Scarlet Nazarian ◽  
Ben Glover ◽  
Hutan Ashrafian ◽  
Ara Darzi ◽  
Julian Teare
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals The diagnostic accuracy of artificial intelligence and computer-aided diagnosis for the detection and characterisation of colorectal polyps: A systematic review and meta-analysis. (Preprint)

2021 ◽  
Author(s):  
Scarlet Nazarian ◽  
Ben Glover ◽  
Hutan Ashrafian ◽  
Ara Darzi ◽  
Julian Teare
Keyword(s):  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Cochrane Library ◽  
Polyp Detection ◽  
Adenoma Detection ◽  
Computer Aided ◽  
The Cochrane Library ◽  
Aided Diagnosis

BACKGROUND Colonoscopy reduces the incidence of colorectal cancer by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of AI technologies to tackle the issues around missed polyps and as a tool to increase adenoma detection rate (ADR). OBJECTIVE The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps. METHODS A comprehensive literature search was undertaken using the databases of EMBASE, Medline and the Cochrane Library. PRISMA guidelines were followed. Studies reporting use of computer-aided diagnosis for polyp detection or characterisation during colonoscopy were included. Independent proportion and their differences were calculated and pooled through DerSimonian and Laird random-effects modelling. RESULTS A total of 48 studies were included. The meta-analysis showed a significant increase in pooled PDR in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (OR 1.75; 95% CI 1.56-1.96; p= 0.0005). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53; 95% CI 1.32-1.77; p= 0005). CONCLUSIONS With the aid of machine learning, there is potential to improve ADR and consequently reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterisation of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians. CLINICALTRIAL Prospero registration - CRD42020169786

scholarly journals The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1759 ◽  
Author(s):  
Nonhlanhla Chambara ◽  
Michael Ying
Keyword(s):  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Performance Outcomes ◽  
Computer Aided Diagnosis ◽  
Current Evidence ◽  
Grey Scale ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.

Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared to intuitive optical diagnosis

Endoscopy ◽  
2020 ◽  
Author(s):  
Quirine E.W. van der Zander ◽  
Ramon Michel Schreuder ◽  
Roger Fonollà ◽  
Thom Scheeve ◽  
Fons van der Sommen ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Multimodal Imaging ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Optical Diagnosis ◽  
High Definition ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Optical diagnosis of colorectal polyps (CRPs) remains challenging. Imaging enhancement techniques such as narrow band imaging and blue light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high definition white light (HDWL) and BLI images, and compared it with the optical diagnosis of expert and novice endoscopists. Methods: The CADx characterized CRPs by exploiting artificial neural networks. Six experts and thirteen novices optically diagnosed 60 CRPs based on intuition. After a washout period of four weeks, the same set of CRPs was permuted and optically diagnosed using BASIC (BLI Adenoma Serrated International Classification). Results: The CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy, combining HDWL and BLI (multimodal imaging), was 95.0% and significantly higher compared to experts (81.7%, p=0.031) and novices (66.5%, p<0.001). Sensitivity (95.6% vs. 61.1% and 55.4%) was also higher for CADx, while specificity was higher for experts compared to CADx and novices (94.1% vs 93.3% and 92.1%). For endoscopists, diagnostic accuracy did not increase using BASIC, neither for experts (Intuition 79.5% vs BASIC 81.7%, p=0.140) nor for novices (Intuition 66.7% vs BASIC 66.5%, p=0.953). Conclusion: The CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of CRPs. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of the CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared to intuitive optical diagnosis.

scholarly journals A Systematic Review on the Role of Artificial Intelligence in Sonographic Diagnosis of Thyroid Cancer: Past, Present and Future

2021 ◽  
Author(s):  
Fatemeh Abdolali ◽  
Atefeh Shahroudnejad ◽  
Sepideh Amiri ◽  
Abhilash Rakkunedeth Hareendranathan ◽  
Jacob L Jaremko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Thyroid Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Sonographic Diagnosis ◽  
Computer Aided ◽  
Diagnosis Of Thyroid Cancer ◽  
Aided Diagnosis

Thyroid cancer is common worldwide with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration to determine whether the nodule is malignant. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems aimed at characterizing thyroid nodules based on ultrasound scans. Earlier approaches along these lines relied on clinically relevant features that were manually identified by radiologists. With the recent success of Artificial Intelligence (AI), various new methods using deep learning are being developed to identify these features in thyroid ultrasound automatically. In this paper, we present a systematic review of state-of-the-art on Artificial Intelligence (AI) application in sonographic diagnosis of thyroid cancer. This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis, from methods using feature-based models to the most recent deep learning-based approaches. In this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.

scholarly journals 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 ◽  
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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