Computer Assisted Detection of Neoplasia During Colonoscopy Evaluation
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Neoplasms
PROVIDER: 62281 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR).
Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.
| 8858 | ecrin-mdr-crc
Project description:COLO-DETECT is a clinical trial to evaluate whether an Artificial Intelligence device ("GI Genius", manufactured by Medtronic) can identify more polyps (pre-cancerous growths of the bowel lining) during colonoscopy (large bowel camera test) than during colonoscopy without it.
| 2365002 | ecrin-mdr-crc
Project description:The development of artificial intelligence (AI) systems in the field of colorectal endoscopy is currently booming, colorectal cancer being, by its frequency and severity, a real public health problem.
In terms of image analysis, AI is indeed able to perform many tasks simultaneously (lesion detection, classification, and segmentation) and to combine them.
Lesion detection is thus the starting point of the whole chain to choose at the end the most appropriate treatment for the patient. Large-scale studies have demonstrated the superiority of artificial intelligence-assisted detection over the usual detection by gastroenterologists, mainly for the detection of sub-centimeter polyps.
However, the investigators have shown that a recent computer-aided detection system (CADe) such as the ENDO-AID software in combination with the EVIS X1 video column (Olympus, Tokyo, Japan) may present difficulties in the detection of flat lesions such as sessile serrated lesions (SSLs) and non-granular laterally spreading tumors (LST-NGs).
This represents a major challenge because in addition to their shape being difficult to identify for the human eye in practice and where AI assistance would be of great value, these rare lesions are associated with advanced histology.
In addition, the investigators recently described the case of a worrisome false negative of AI-assisted colonoscopy, which failed to detect a flat adenocarcinoma in the transverse colon.
Therefore, it is important to measure the false negative rate of AI detection based on the macroscopic shape of the lesion. Comparing this rate to the human endoscopist’s false negatives would improve the performance of AI for this specific lesion subtype in the future.
| 66296 | ecrin-mdr-crc
Project description:BackgroundEvidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy.AimThe detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated.MethodsA previously validated deep learning CADe system for polyp detection was tested exclusively on LSTs and SSA/Ps. 1451 LST images from 184 patients were collected between July 2015 and January 2019, 82 SSA/Ps videos from 26 patients were collected between September 2018 and January 2019. The per-frame sensitivity and per-lesion sensitivity were calculated.Results(1) For LSTs image dataset, the system achieved an overall per-image sensitivity and per-lesion sensitivity of 94.07% (1365/1451) and 98.99% (197/199) respectively. The per-frame sensitivity for LST-G(H), LST-G(M), LST-NG(F), LST-NG(PD) was 93.97% (343/365), 98.72% (692/701), 85.71% (324/378) and 85.71% (6/7) respectively. The per-lesion sensitivity of each subgroup was 100.00% (71/71), 100.00% (64/64), 98.31% (58/59) and 80.00% (4/5). (2) For SSA/Ps video dataset, the system achieved an overall per-frame sensitivity and per-lesion sensitivity of 84.10% (15883/18885) and 100.00% (42/42), respectively.ConclusionsThis study demonstrated that a local-feature-prioritized automatic CADe system could detect LSTs and SSA/Ps with high sensitivity. The per-frame sensitivity for non-granular LSTs and small SSA/Ps should be further improved.
| S-EPMC7173785 | biostudies-literature
Project description:BackgroundThe use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy.MethodsUsing Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658).FindingsThis study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%).InterpretationAI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence.FundingThis work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).
| S-EPMC10698672 | biostudies-literature
Project description:Evaluation of the colonic mucosa with a high definition colonoscope (EPKi7010 video processor).
The endoscopy images will be seen on a 27inch, flat-panel, high-definition LCD monitor (Radiance ultraSC-WU27-G1520 model) only by one expert endoscopist, randomly assigned.
The number, location, and polyps’ features (Paris classification) will be recorded by the operator. If a polyp is detected, the endoscopist will remove the polyp endoscopically with a cold snare.
The same patient will be submitted to a second, the same session, computed aided real-time colonoscopy using the DISCOVERY, AI-assisted polyp detector. Colonoscopy will be performed by a same-level-of-expertise operator in comparison to the initial procedure. Any polyp or lesion detected with the AI system will be recorded and endoscopically removed and considered as a missed lesion from standard colonoscopy.
| 2379760 | ecrin-mdr-crc
Project description:The goal of this substudy is to investigate the accuracy of a computer-aided polyp characterization (CADx) system. The main question[s] it aims to answer are:
• How high is the specificity of the AI system when characterizing colorectal polyps
Participants will receive a standard colonoscopy, assisted by the artificial intelligence (AI) assisted system GI Genius.
Researchers will compare the AI system´s characterization with the histopathology to see how accurate the system is.
| 10350 | ecrin-mdr-crc
Project description:Background and study aims Better patient education prior to colonoscopy improves adherence to instructions for bowel preparation and leads to cleaner colons. We reasoned that computer assisted instruction (CAI) using video and 3 D animations followed by nurse contact maximizes the effectiveness of nurse counselling, increases proportion of clean colons and improves patient experience.Patients and methods Adults referred for colonoscopy in a high-volume endoscopy unit in the Netherlands were included. Exclusion criteria were illiteracy in Dutch and audiovisual handicaps. Patients were prospectively divided into 2 groups, 1 group received nurse counselling and 1 group received CAI and a nurse contact before colonoscopy. The main outcome, cleanliness of the colon during examination, was measured with Ottawa Bowel Preparation Scale (OBPS) and Boston Bowel Preparation Scale (BBPS). We assessed patient comfort and anxiety at 3 different time points.Results We included 385 patients: 197 received traditional nurse counselling and 188 received CAI. Overall patient response rates were 99 %, 76.4 % and 69.9 % respectively. Endoscopists scored cleanliness in 60.8 %. Comparative analysis of the 39.2 % of patients with missing scores showed no significant difference on age, gender or educational level. Baseline characteristics were evenly distributed over the groups. Bowel cleanliness was satisfactory and did not differ amongst groups: nurse vs. CAI group scores in BBPS: (6.54 ± 1.69 vs. 6.42 ± 1.62); OBPS: (6.07 ± 2.53 vs. 5.80 ± 2.90). Patient comfort scores were significantly higher (4.29 ± 0.62 vs. 4.42 ± 0.68) in the CAI group shortly before colonoscopy. Anxiety and knowledge scores were similar.Conclusion CAI is a safe and practical tool to instruct patients before colonoscopy. We recommend the combination of CAI with a short nurse contact for daily practice.
| S-EPMC5546893 | biostudies-literature
Project description:The focus of the study is to evaluate impact on Adenomas Per Colonoscopy (APC) with a Computer Aided Detection (CAD) software assisting the gastroenterologist during a colonoscopy procedure.
| 2324429 | ecrin-mdr-crc
Project description:BackgroundLongstanding colonic IBD increases the risk of developing colorectal cancer. The utility of chromoendoscopy with standard-definition white light technology has been established. However, the use of high-definition virtual chromoendoscopy (HDV) in colitis surveillance remains undefined.ObjectiveTo compare the performance of HDV (i-scan OE mode 2) with high-definition white light (HDWL) for detection of neoplasia in patients with IBD undergoing surveillance colonoscopy. Additionally, we assessed the utility of protocol-guided quadrantic non-targeted biopsies.DesignA multioperator randomised controlled trial was carried out in two centres in the UK. Total of 188 patients (101 men, mean age 54) with longstanding ulcerative or Crohn's colitis were randomised, prior to starting the surveillance colonoscopy, to using either HDV (n=94) or HDWL (n=94) on withdrawal. Targeted and quadrantic non-targeted biopsies were taken in both arms per-randomisation protocol. The primary outcome was the difference in neoplasia detection rate (NDR) between HDV and HDWL.ResultsThere was no significant difference between HDWL and HDV for neoplasia detection. The NDR was not significantly different for HDWL (24.2%) and HDV (14.9%) (p=0.14). All intraepithelial neoplasia (IEN) detected contained low-grade dysplasia only. A total of 6751 non-targeted biopsies detected one IEN only. The withdrawal time was similar in both arms of the study; median of 24 min (HDWL) versus 25.5 min (HDV).ConclusionHDV and HDWL did not differ significantly in the detection of neoplasia. Almost all neoplasia were detected on targeted biopsy or resection. Quadrantic non-targeted biopsies have negligible additional gain.Trial registration numberClinical Trial.gov ID NCT02822352.
| S-EPMC8355878 | biostudies-literature