Discover our Clinical Publications

Avicenna.AI is dedicated to pushing boundaries of healthcare through Clinical publications to share the value of AI-tools in Radiology. Delve into our varied clinical publications, spanning validation studies and performance assessment.

Join our vibrant research community! We welcome researchers, and clinicians, to engage to collaborate with us.

CINA-iPE

Performance Evaluation of an Artificial Intelligence (AI)-based Algorithmfor Incidental Findings of Pulmonary Embolism

This study aims to evaluate the performance of CINA-iPE in detecting unsuspected pulmonary embolism, where missed PE rates remain high and undiagnosed PE can be fatal in up to 25% of cases.

Performance Evaluation of an Artificial Intelligence (AI)-based Algorithmfor Incidental Findings of Pulmonary Embolism

A. Ayobi, J. Schlossman, S. Salehi, A. Franciosini, M. Scudeler, S. Quenet, Y. Chaibi, D. Chow, P. Chang, B. Bista, A. Imanzadeh
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CINA-VCF

Performance evaluation of an AI-based application for opportunistic screening of thoraco-lumbar vertebral compression fractures

The DL-based algorithm accurately detects vertebral compression fractures (VCF), matching expert assessments. This automation saves radiologists time, enabling earlier osteoporosis diagnosis and treatment.

Performance evaluation of an AI-based application for opportunistic screening of thoraco-lumbar vertebral compression fractures

M. Quemeneur, P. Champsaur, A. Ayobi, C. Charlotte, S. Quenet, J. Kiewsky, M. Mahfoud, C. Avare, D. Guenoun
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CINA-ASPECTS/ CINA-ICH / CINA-LVO

Performance of an AI-based automated identification of ischemic and hemorrhagic stroke in clinical routine

This study aims to evaluate the performance of an FDA-cleared and/or CE-marked AI-based application in identifying intracranial hemorrhage (ICH) and large vessel occlusion (LVO), and in computing the ASPECT Score to support clinicians in stroke imaging workflows.

Performance of an AI-based automated identification of ischemic and hemorrhagic stroke in clinical routine

A.-A. El-Ahmadi, G. Brun, A. Ayobi, S. Quenet, Y. Chaibi, A. Reyre, A. Jacquier, N. Girard
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CINA-ICH / CINA-LVO

Validation of a Deep Learning Tool in the Detection of Intracranial Hemorrhage and Large Vessel Occlusion

The aim of this study was to validate a commercially available deep learning-based tool in the detection of both ICH and LVO across multiple hospital sites and vendors throughout the U.S.

Validation of a Deep Learning Tool in the Detection of Intracranial Hemorrhage and Large Vessel Occlusion

McLouth J, Elstrott S, Chaibi Y, Quenet S, Chang P, Chow D, Soun J.
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CINA-ICH

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

This study aimed to assess the ability of Canon's AUTOStroke Solution ICH detection algorithm to accurately identify patients both with and without ICHs present.

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

Rava RA, Seymour SE, LaQue ME, Peterson BA, et al.
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CINA-LVO

Validation of an Artificial Intelligence Driven Large Vessel Occlusion Detection Algorithm for Acute Ischemic Stroke Patients

We aimed to assess the ability of Canon’s AUTOStroke Solution LVO application in properly detecting and locating LVOs in AIS patients.

Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients

Rava RA, Peterson BA, Seymour SE, Snyder KV, Mokin M, Waqas M, Hoi Y, Davies JM, Levy EI, Siddiqui AH, Ionita CN
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CINA-LVO

Head-to-head comparison of commercial artificial intelligence solutions for detection of large vessel occlusion at a comprehensive stroke center

The purpose of this study is to compare and validate the performance of two AI-based tools (RAPID LVO and CINA LVO) for LVO detection.

Head-to-head comparison of commercial artificial intelligence solutions for detection of large vessel occlusion at a comprehensive stroke center

Schlossman N, Jacob, RO, Daniel, Salehi, Shirin, et al
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CINA-ASPECTS

Validation of a Deep Learning AI-based Software for Automated ASPECTS Assessment

A retrospective, multicenter, multinational, multivendor and blinded study was conducted to evaluate the standalone performance of CINA-ASPECTS.

Validation of a Deep Learning AI-based Software for Automated ASPECTS Assessment

Ayobi A, Chang P, Chow D, Filippi C, Quenet S, Tassy M, Chaibi Y.
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CINA-LVO

Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection

The aim of this study was to assess the diagnostic performance of the AP for the detection of intracranial large vessel occlusions (LVO) on conventional CTA, as well as the duration of CT processing, in a cohort of acute stroke patients.

Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection

Sander E. Temmen, Marinus J. Becks, Steven Schalekamp, Kicky G. van Leeuwen, Frederick J. A. Meijer
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CINA-PE

A deep learning-based algorithm improves radiology residents’ diagnoses of acute pulmonary embolism on CT pulmonary angiograms

This study aimed to compare the diagnostic performance of radiology residents in detecting pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with and without the support of a deep-learning (DL)–based algorithm.

A deep learning-based algorithm improves radiology residents’ diagnoses of acute pulmonary embolism on CT pulmonary angiograms

Alexandre Vallée, Raphaelle Quint, Anne Laure Brun , François Mellot , Philippe A Grenier
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CINA-PE

Performance Evaluation of an Artificial Intelligence (AI)-based Algorithm for Incidental Findings of Pulmonary Embolism

This study assesses an AI tool's ability to detect pulmonary embolism in contrast-enhanced CT scans obtained for non-PE-related conditions.

Performance Evaluation of an Artificial Intelligence (AI)-based Algorithm for Incidental Findings of Pulmonary Embolism

A. Ayobi, J. Schlossman, S. Salehi, A. Franciosini, M. Scudeler, S. Quenet, Y. Chaibi, D. Chow, P. Chang, B. Bista, A. Imanzadeh
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CINA-PE

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms

The objective of this study was to validate the performance of CINA-PE in detecting suspected PEs on CTAs.

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms

Grenier PA, Ayobi A, Quenet S, Tassy M, Marx M, Chow DS, Weinberg BD, Chang PD, Chaibi Y
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CINA-PE

Validation Of A Deep Learning Tool For Automatic Pulmonary Embolism Detection

This study aims to assess the commercially available and CE-marked deep learning-based tool, CINA CHEST, in detecting PE across data inputs from 5 CT vendors.

Validation Of A Deep Learning Tool For Automatic Pulmonary Embolism Detection

J. Schlossman, S. Salehi, B. Weinberg, D. Chow, M. Tassy, S. Quenet, A. Ayobi, Y. Chaibi, P. Chang.
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