AI Detection for Cardiovascular Disease Prevention
(AI INFORM Trial)
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Brigham and Women's Hospital
Disqualifiers: Coronary artery disease, Cancer, others
No Placebo Group
Approved in 1 Jurisdiction
Trial Summary
What is the purpose of this trial?AI INFORM is a multicenter randomized trial that will test the hypothesis that providing clinicians information on the presence and amount of coronary artery calcifications (CAC), will result in initiation or intensification of preventive therapies. The study will use a cloud-based artificial intelligence (AI) platform (Nanox.AI) that can analyze non contrast chest CT and estimate the amount of CAC.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the treatment Nanox.AI Coronary Artery Calcification Assessment for cardiovascular disease prevention?
Research shows that AI-based systems can automatically and accurately measure coronary artery calcium, which is a strong predictor of heart-related events. This automated approach is efficient and correlates well with manual methods, suggesting it could help in early detection and prevention of cardiovascular diseases.
12345Is the AI Detection for Cardiovascular Disease Prevention safe for humans?
The research articles focus on the accuracy and reliability of AI systems in detecting coronary calcifications using CT scans, but they do not provide specific safety data for human use of the AI technology itself.
46789How does the AI-based treatment for cardiovascular disease prevention differ from other treatments?
This AI-based treatment is unique because it uses artificial intelligence to automatically detect and quantify coronary artery calcium from CT scans, which helps in assessing the risk of cardiovascular disease more efficiently and accurately compared to traditional methods that rely on manual analysis.
125910Eligibility Criteria
This trial is for individuals aged 40-75 who've had a chest CT scan in the last 3 years and have no history of coronary artery disease, cancer, or any other life-limiting conditions. It's aimed at enhancing cardiovascular disease prevention.Inclusion Criteria
I am between 40-75 years old and had a chest CT scan in the last 3 years.
Exclusion Criteria
I do not have any other condition that could shorten my life.
I have had heart disease related to my arteries.
I have had cancer before.
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Intervention
Notification or non-notification of coronary artery calcification detected by AI, followed by recommendation or non-recommendation of preventive therapy
6 months
Follow-up
Participants are monitored for changes in LDL-C, initiation or intensification of preventive therapies, and occurrence of cardiovascular events
12 months
Participant Groups
The AI INFORM study is testing if notifying clinicians about the presence and amount of calcium buildup in arteries using an AI tool (Nanox.AI) leads to better preventive care. This involves analyzing past chest CT scans without contrast.
2Treatment groups
Experimental Treatment
Active Control
Group I: Notification of Coronary Artery CalcificationExperimental Treatment1 Intervention
Notification to providers of the presence of coronary artery calcification automatically detected by AI based device (software) on chest CT.
Recommendation of preventive therapy.
Group II: Non-notification of Coronary Artery CalcificationActive Control1 Intervention
No notification to providers of the presence of coronary artery calcification on chest CT automatically detected by AI based device (software) on chest CT.
No recommendation of preventive therapy.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Brigham and Women's HospitalBoston, MA
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Who Is Running the Clinical Trial?
Brigham and Women's HospitalLead Sponsor
Nano-X Imaging LimitedCollaborator
References
Evaluation of an AI-based, automatic coronary artery calcium scoring software. [2021]To evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference.
Machine Learning-based Algorithm Enables the Exclusion of Obstructive Coronary Artery Disease in the Patients Who Underwent Coronary Artery Calcium Scoring. [2020]An application of artificial intelligence to screen for obstructive coronary artery disease (CAD) after coronary artery calcium scoring (CACS) test.
Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT. [2023]To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard.
Deep convolutional neural networks to predict cardiovascular risk from computed tomography. [2022]Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method. [2023]To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images.
Detection of heart calcification with electron beam CT: interobserver and intraobserver reliability for scoring quantification. [2016]To assess interobserver and intraobserver reliability of three quantitative measures of coronary artery calcium burden: calcium "score," number of calcified lesions, and calcified area.
[Detection and quantification of coronary calcification: an update]. [2016]The demonstration of calcification of the coronary arterial wall indicates the presence of coronary heart disease (CHD). The prevalence of coronary calcifications increases with age. The extent of the calcifications correlates with the total coronary plaque burden and with the probability of a future myocardial infarction in symptomatic patients. In asymptomatic subjects with risk factors for a myocardial event demonstration of coronary calcifications is diagnostic for coronary atherosclerotic disease before clinical manifestation of the disease. Exact quantification of coronary arterial calcifications (calcium scoring) has become possible with electron beam computed tomography (EBCT) or ECG triggered subsecond CT scanners. Further improvements in the detection of coronary calcifications can be expected with the introduction of multi-slice CT. The prognostic relevance of coronary calcium scoring in asymptomatic high-risk patients is not yet clearly defined. It remains to be clarified whether newer, volume based methods of calcium quantification will be superior to the classic calcium score (Agatston-Score) for risk assessment and follow-up in this patient group.
Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions. [2019]Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.
Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease. [2019]A fully automated method for coronary calcification detection from non-contrast-enhanced, ECG-gated multi-slice computed tomography (CT) data is presented. Candidates for coronary calcifications are extracted by thresholding and component labeling. These candidates include coronary calcifications, calcifications in the aorta and in the heart, and other high-density structures such as noise and bone. A dedicated set of 64 features is calculated for each candidate object. They characterize the object's spatial position relative to the heart and the aorta, for which an automatic segmentation scheme was developed, its size and shape, and its appearance, which is described by a set of approximated Gaussian derivatives for which an efficient computational scheme is presented. Three classification strategies were designed. The first one tested direct classification without feature selection. The second approach also utilized direct classification, but with feature selection. Finally, the third scheme employed two-stage classification. In a computationally inexpensive first stage, the most easily recognizable false positives were discarded. The second stage discriminated between more difficult to separate coronary calcium and other candidates. Performance of linear, quadratic, nearest neighbor, and support vector machine classifiers was compared. The method was tested on 76 scans containing 275 calcifications in the coronary arteries and 335 calcifications in the heart and aorta. The best performance was obtained employing a two-stage classification system with a k-nearest neighbor (k-NN) classifier and a feature selection scheme. The method detected 73.8% of coronary calcifications at the expense of on average 0.1 false positives per scan. A calcium score was computed for each scan and subjects were assigned one of four risk categories based on this score. The method assigned the correct risk category to 93.4% of all scans.
Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography. [2023]Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.