Artificial Intelligence to Solve the MissINg of Gastric Cancer (AIMING)
ConditionGastric Cancer
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age60+
SponsorIstituto Clinico Humanitas
About this trial
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).
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
All >60 years-old patients undergoing upper-gastrointestinal (GI) endoscopy for selected indications at high-risk of gastric cancer..
Disqualifiers
contraindications to upper-GI endoscopy.
contraindications to biopsy.
active upper-GI bleeding or urgent upper-GI endoscopy.
patients with previous upper-GI surgery involving the stomach.
Trial design
Design model
Parallel
Treatments tested in this trial
Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
DeviceTwo 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.
Treatment groups
6,600 Participants
are divided into 4 treatment groupsGroup A: Parallel arm 1No intervention 0 interventions
Group B: Parallel arm 2Active comparator 1 intervention
Group C: Cross-over arm 1 (control)Other 1 intervention
Group D: Cross-over arm 2Active comparator 1 intervention
Trial outcomes
Primary outcomes
1
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 enrollmentSecondary outcomes
1
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 years2
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 enrollmentLocations
Sponsors and contacts
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Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: