Real-time artificial intelligence–based histologic classification of colorectal polyps with augmented visualization

Rodriguez‐Diaz, Eladio; Baffy, György; Lo, Wai–Kit; Mashimo, Hiroshi; Vidyarthi, Gitanjali; Mohapatra, Shyam S.; Singh, Satish K.

doi:10.1016/j.gie.2020.09.018

Cited by 49 publications

(55 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the intent of directing the rapid endoscopic technology development, the Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) initiative (an American Society for Gastrointestinal Endoscopy (ASGE) program) suggested that a novel technology should achieve a threshold of negative predictive value (NPV) >90% for the optical diagnosis of diminutive colorectal polyps [92]. A summary of recent studies on AI systems for colorectal polyp characterization is presented in Table 2 [49,83,[93][94][95][96][97][98][99][100][101][102][103][104][105][106][107].…”

Section: Polyp Characterizationmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

View full text Add to dashboard Cite

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

show abstract

Section: Polyp Characterizationmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Rodriguez-Diaz et al [ 91 ] developed a DL model to locate areas of malignant transformation inside polyps using semantic segmentation, distinguishing between neoplastic and non-neoplastic polyps, with a sensitivity of 0.96 and a specificity of 0.84[ 91 ]. Haj-Hassan et al [ 92 ] used a CNN to classify segmented regions of interest into three tissue types related to CRC progression (benign hyperplasia, intraepithelial neoplasia and carcinoma), with an accuracy of 99.17%[ 92 ].…”

Section: Applications Of Ai In Gi Pathologymentioning

confidence: 99%

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Berbís¹,

Aneiros‐Fernández²,

Olivares³

et al. 2021

WJG

View full text Add to dashboard Cite

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization ( i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

show abstract

“…Among these, 98 articles were excluded from the final enrollment for the following reasons: 73 (74%) for incomplete data, 11 (11%) for narrative review, 8 (8%) for study protocol, and 6 (6%) for systematic review or meta-analysis. Finally, 13 studies [21][22][23][24][25][26][27][28][29][30][31][32][33] were included in the systematic review. A flowchart of the selection process is presented in Figure 1.…”

Section: Study Selectionmentioning

confidence: 99%

“…Seven studies [24,26,27,[29][30][31][32] used endoscopic images from Asian populations, and six studies [21][22][23]25,28,33] used endoscopic images from Western populations. All included studies adopted the definition of DCPs as size <5 mm.…”

Section: Clinical Features In Included Studiesmentioning

confidence: 99%

“…However, Shahidi et al [23] adopted a stricter definition for DCPs with a size <3 mm. With regard to the type of CAD model, a deep neural network or convolutional neural network was used in six studies [21][22][23][24][25]30], a support vector machine in six studies [27][28][29][31][32][33], and a software-based automatic color intensity analysis in one study [26]. White-light imaging is currently the standard method for inspecting endoscopic lesions.…”

Section: Clinical Features In Included Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Bang¹,

Lee²,

Baik³

2021

J Med Internet Res

View full text Add to dashboard Cite

Background Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. Objective This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. Methods Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. Results Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. Conclusions CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. Trial Registration PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189

show abstract

Real-time artificial intelligence–based histologic classification of colorectal polyps with augmented visualization

Cited by 49 publications

References 35 publications

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Contact Info

Product

Resources

About