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.

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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

Artificial Intelligence and Acute Stroke Imaging

The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence–driven applications for acute stroke triage, surveillance, and prediction.

Artificial Intelligence and Acute Stroke Imaging

Jennifer E. Soun, Daniel S. Chow, M. Nagamine, R.S. Takhtawala, C.G. Filippi, W. Yu and Peter Chang

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.

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

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

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.

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.

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

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time

A multi-reader, multi-case study in which readers assessed ASPECTS without and with the support of a deep learning (DL)-based algorithm in order to analyze the impact of the software on clinicians' performance and interpretation time.

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time

Angela Ayobi, Adam Davis, Peter D Chang, Daniel S Chow, Kambiz Nael, Maxime Tassy, Sarah Quenet, Sylvain Fogola, Peter Shabe, David Fussell, Christophe Avare and Yasmina Chaibi

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection

This study assesses the performance of an AI-based application, CINA-PE, in detecting pulmonary embolism (PE) on CT pulmonary angiography (CTPA), using data from 230 US cities. The algorithm's results were compared with the consensus of three expert radiologists to evaluate its accuracy and clinical utility.

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection

A. Ayobi ∙ P.D. Chang ∙ D.S. Chow ∙ B.D. Weinberg ∙ M. Tassy ∙ A. Franciosini ∙ M. Scudeler ∙ S. Quenet ∙ C. Avare ∙ Y. Chaibi

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

Performance Evaluation of an Artificial Intelligence (AI)-based Algorithm for 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 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

Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans

The purpose of this paper is to assess the value of adding CINA-iPE, an AI system for the detection of unsuspected PEs on chest CT scans, in routine oncology care outside urgent medical conditions.

Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans

S. Ammari, A.Orfali Camez, A.Ayobi, S. Quenet, A. Zemmouri, E. Mniai, Y. Chaibi, A.Franciosini, L.Clavel, F. Bidault, S. Muller, N.Lassau, C.Balleyguier and T.Assi

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

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.

Diagnostic Performance for a Aortic Dissection Triage Prioritization and Classification

This multi-center retrospective study assessed a deep learning application's ability to detect, classify, and highlight suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans.

Diagnostic Performance for a Aortic Dissection Triage Prioritization and Classification

V. Laletin, A. Ayobi, P. Chang, D. Chow, J. Soun, J.C. Junn, M. Scudeler, S. Quenet, M. Tassy, C. Avare, M. Roca, Y. Chaibi

Algorithmic performance consistency across patient demographics and scanner manufacturers

Shirin Salehi; Jacob Schlossman; Saba Chowdhry; Marlene SCUDELER; Sarah Quenet, BS; Angela Ayobi, Yasmina Chaibi, PhD; Peter Chang,

Performance evaluation of an AI-based application for opportunistic screening of thoraco-lumbar VCF

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 VCF

M. Quemeneur, P. Champsaur, A. Ayobi, C. Charlotte, S. Quenet, J. Kiewsky, M. Mahfoud, C. Avare, D. Guenoun

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

To evaluate the performance of an artificial intelligence (AI)-based application designed to automatically screen CT scans with unsuspected VCF in order to assist physicians in the assessments of musculoskeletal diseases.

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

A. Ayobi, D. Chow, J. Soun, P. Chang, C. Castineira, J. Kiewsky, M. Mahfoud, C. Avare, Y. Chaibi

Discover our Clinical Publications

CINA-ASPECTS/ CINA-ICH / CINA-LVO

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

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

CINA-ASPECTS/ CINA-ICH / CINA-LVO

Artificial Intelligence and Acute Stroke Imaging

Artificial Intelligence and Acute Stroke Imaging

CINA-ICH

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

CINA-LVO

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

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

CINA-LVO

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

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

CINA-ASPECTS

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

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

CINA-ICH / CINA-LVO

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

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

CINA-LVO

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

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

CINA-ASPECTS

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time

CINA-PE

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection

CINA-PE

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

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

CINA-iPE

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

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

CINA-iPE

Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans

Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans

CINA-PE

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

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

CINA-PE

Validation Of A Deep Learning Tool For Automatic Pulmonary Embolism Detection

Validation Of A Deep Learning Tool For Automatic Pulmonary Embolism Detection

CINA-AD

Diagnostic Performance for a Aortic Dissection Triage Prioritization and Classification

Diagnostic Performance for a Aortic Dissection Triage Prioritization and Classification

CINA-AD

Algorithmic performance consistency across patient demographics and scanner manufacturers

Algorithmic performance consistency across patient demographics and scanner manufacturers

CINA-VCF

Performance evaluation of an AI-based application for opportunistic screening of thoraco-lumbar VCF

Performance evaluation of an AI-based application for opportunistic screening of thoraco-lumbar VCF

CINA-VCF

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

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

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