ReviewMedicina (Kaunas, Lithuania)2019
Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do?
Review in Medicina (Kaunas, Lithuania), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis.International journal of colorectal disease · 2021Pooled it
- Efficacy and Safety of Linaclotide as an Adjunct to Polyethylene Glycol in Bowel Preparation: A Meta-Analysis.Journal of digestive diseasesPooled it
- Development and early feasibility testing of machine-learning algorithms to non-invasively assess hemoglobin levels.npj biomedical innovations · 2026Article
- Thyroid nodules as predictors of adenomatous colonic polyps: A systematic review and meta-analysis.World journal of clinical cases · 2025Article
- Applying Deep-Learning Algorithm Interpreting Kidney, Ureter, and Bladder (KUB) X-Rays to Detect Colon Cancer.Journal of imaging informatics in medicine · 2025Article
- Exploring the Mechanism of Canmei Formula in Preventing and Treating Recurrence of Colorectal Adenoma Based on Data Mining and Algorithm Prediction.Biological procedures online · 2025Article
- Water-assisted colonoscopy in inflammatory bowel diseases: From technical implications to diagnostic and therapeutic potentials.World journal of gastrointestinal endoscopy · 2024Review
- A Novel Soft and Inflatable Strain-based Tactile Sensing Balloon for Enhanced Diagnosis of Colorectal Cancer Polyps Via Colonoscopy.IEEE sensors journal · 2024Article
- A Reliable and Sensitive Framework for Simultaneous Type and Stage Detection of Colorectal Cancer Polyps.Annals of biomedical engineering · 2023Article
- The role of deep learning in diagnosing colorectal cancer.Przeglad gastroenterologiczny · 2023Review
- A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).Frontiers in oncology · 2023Article
- A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques.Healthcare (Basel, Switzerland) · 2022Review
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- Simplifying Measurement of Adenoma Detection Rates for Colonoscopy.Digestive diseases and sciences · 2021Article
- Patient, Physician, and Procedure Characteristics Are Independently Predictive of Polyp Detection Rates in Clinical Practice.Digestive diseases and sciences · 2021Article
- Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach.Diagnostics (Basel, Switzerland) · 2021Article
- [Application of artificial intelligence for detection and classification of colon polyps].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Review
- Deep Learning in Selected Cancers' Image Analysis-A Survey.Journal of imaging · 2020Review
- Intratumoral Cytotoxic T-Lymphocyte Density and PD-L1 Expression Are Prognostic Biomarkers for Patients with Colorectal Cancer.Medicina (Kaunas, Lithuania) · 2019Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Colorectal cancer (CRC) is one of the most common causes of cancer mortality in the world. The incidence is related to increases with age and western dietary habits. Early detection through screening by colonoscopy has been proven to effectively reduce disease-related mortality. Currently, it is generally accepted that most colorectal cancers originate from adenomas. This is known as the "adenoma-carcinoma sequence", and several studies have shown that early detection and removal of adenomas can effectively prevent the development of colorectal cancer. The other two pathways for CRC development are the Lynch syndrome pathway and the sessile serrated pathway. The adenoma detection rate is an established indicator of a colonoscopy's quality. A 1% increase in the adenoma detection rate has been associated with a 3% decrease in interval CRC incidence. However, several factors may affect the adenoma detection rate during a colonoscopy, and techniques to address these factors have been thoroughly discussed in the literature. Interestingly, despite the use of these techniques in colonoscopy training programs and the introduction of quality measures in colonoscopy, the adenoma detection rate varies widely. Considering these limitations, initiatives that use deep learning, particularly convolutional neural networks (CNNs), to detect cancerous lesions and colonic polyps have been introduced. The CNN architecture seems to offer several advantages in this field, including polyp classification, detection, and segmentation, polyp tracking, and an increase in the rate of accurate diagnosis. Given the challenges in the detection of colon cancer affecting the ascending (proximal) colon, which is more common in women aged over 65 years old and is responsible for the higher mortality of these patients, one of the questions that remains to be answered is whether CNNs can help to maximize the CRC detection rate in proximal versus distal colon in relation to a gender distribution. This review discusses the current challenges facing CRC screening and training programs, quality measures in colonoscopy, and the role of CNNs in increasing the detection rate of colonic polyps and early cancerous lesions.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.