ArticleDiagnostics (Basel, Switzerland)2021
Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Automated identification of keratinocyte cancers in pathology reports using large language models.PLOS digital health · 2026Article
- A Double-Thiadiazole-Derived Fluorescent Probe for the Cyclooxygenase-2 Detection in Colorectal Tumor-Inflammation Interaction.Journal of fluorescence · 2026Article
- Advanced AI-Powered System for Comprehensive Thyroid Cancer Detection and Malignancy Risk Assessment.Life (Basel, Switzerland) · 2025Article
- Comprehensive evaluation and progress of colorectal cancer screening methods.Gut pathogens · 2025Review
- Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.Sensors (Basel, Switzerland) · 2025Review
- A Scoring Method to Prioritize Fecal Occult Blood Testing as a First Step in Colorectal Cancer Screening in Resource-Limited Settings.Diagnostics (Basel, Switzerland) · 2023Article
- A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.BMC bioinformatics · 2023Review
- Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy.Exploration of targeted anti-tumor therapy · 2023Article
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- Modelling in Synthesis and Optimization of Active Vaccinal Components.Nanomaterials (Basel, Switzerland) · 2021Article
- Automatic Detection of Colorectal Polyps Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.
Indexed as
Identifiers
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.