Ischemic stroke, a leading global cause of death and disability, is caused by carotid arteries atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on the panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), and which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approaches followed by eXtreme Gradient Boosting algorithm (XGBoost) that achieved true labels for each corner, and reaches a sensitivity (recall) of 0.82 and a specificity of 0.93 for individual artery, and a recall of 0. 8 8 and specificity of 0.86 for individual patients. Applying and integrating the algorithm we developed in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.

I have read the journal?s policy. All authors are advisors to or employees of a commercial company.

This study was done in a commercial company (self funded).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Poriya Medical Center Institutional Review Board (approval # POR-0008-21) and was performed in accordance with the Declaration of Helsinki, seventh revision (2013).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

The minimal data set cannot be shared publicly as they contain potentially identifying patient information. The data are owned by Yonsei University Dental Hospital. For researchers who meet the criteria for access to confidential data, requests for these data sets can be sent to Yonsei University Dental Hospital IRB Committee via detailirb@yuhs.ac. The authors had no special access privileges that others would not have.