An interventional study of Integration of Artificial Intelligence (AI) assistance to screening gastroscopy in Gastric Cancer, sponsored by Istituto Clinico Humanitas. Not yet recruiting. Open to participants aged 60 Years and older. Per ClinicalTrials.gov, last updated 2025-05-14.
Sponsored by Istituto Clinico Humanitas · Not applicable, Interventional, and Prevention
Our AIMING project comprises four core work packages (WPs): WP1. Nation-level randomized controlled trial; WP2. Development of an innovative AI tool; WP3. Novel microsimulation modelling; WP4. Patient inclusion.
The nation-level multi-center tandem randomized controlled trial (WP1) will contribute to a better understanding of how the real-time AI algorithm can reduce miss rate of early gastric cancer and dysplasia during gastroscopy. Moreover, the innovation project will contribute to development of a novel AI tool (WP2) that can stratify the risk of gastric cancer by identifying in vivo precancerous conditions. Furthermore, a microsimulation modelling will allow us to predict how the use of AI can prevent gastric cancer and affect cost and patients' burdens. The assessment of the balance between benefits and harms is quite crucial especially for this type of medical device because the value of innovative tools is sometimes overestimated due to stakeholders' enthusiasm (WP3). Finally, we will take care of patients' perspective throughout the study project by including patient organization in both WP1, 2, and 3 (WP4).
2,850 studies on the registry are indexed under Stomach Neoplasms; 863 are open to participants now.
This study's planned enrollment of 6,600 is above the median of 67 across 2,095 interventional studies indexed under Stomach Neoplasms.
Browse Stomach Neoplasms studies →Istituto Clinico Humanitas is the lead sponsor of 275 studies on the registry; 71 are open to participants now.
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Exclusion Criteria:
patients will undergo standard high-definition and high-quality upper-GI endoscopy for the detection of gastric lesions with histological mapping according to Sydney system
patients will undergo high-definition and high quality upper-GI endoscopy with real-time assistance by real-time artificial intelligence for the detection of early gastric cancer and gastric dysplasia.
Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
patients will undergo two standard high-definition and high-quality upper-GI endoscopies in tandem: the first will be without Artificial Intelligence assistance, and the second with Artificial Intelligence in order to define the miss rate for standard unassisted upper-GI endoscopy.
Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
patients will undergo two standard high-definition and high-quality upper-GI endoscopies in tandem: the first will be with Artificial Intelligence assistance, and the second without Artificial Intelligence in order to define the decrease of miss rate when assistance by Artificial Intelligence is implemented.
Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
Two novel deep learning systems, namely one for endoscopy and one for pathology, will be trained and validated for the diagnosis of gastric atrophy and metaplasia, including extension and severity. Both of the algorithms will be validated against the cases not used for the training phases. Approximately, the partition will be 5 to 1. The benefit and harm of AI-assistance for early diagnosis of gastric cancer will be simulated by developing a Markov model on the natural history of gastric cancer from dysplasia to early and advanced cancer, as well as by the impact of a GS on its natural history. This will also simulate the potential effect of lead- and length-time bias. These data will be incorporated in the simulation model in order to include them in the decision-making process on whether AI-assistance for gastric cancer detection should be or not recommended to health systems.
Miss rate reduction
change of the miss rate of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (tandem).
Time frame: 2025: 12 months enrollment
Change number of Detections
Change in the detection of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (parallel).
Time frame: 1 day procedure and follow up for 2 years
patient satisfaction
Assessment of patient acceptability, satisfaction and tolerance, assessed by questionnaire, towards AI technology for both the detection and the characterization of gastric lesions.
Time frame: 2025: during the 12 months enrollment
No study locations are listed for this record.
This study is not yet recruiting, as verified in May 2025. You cannot join it, but the record below documents what was studied.
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Istituto Clinico Humanitas