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Application of an artificial intelligence ensemble for detection of important secondary findings on lung ventilation and perfusion SPECT-CT.
Smith, Carter; Nance, Sophia; Chamberlin, Jordan H; Maisuria, Dhruw; O'Doherty, Jim; Baruah, Dhiraj; Schoepf, Uwe Joseph; Szemes, Akos-Varga; Elojeimy, Saeed; Kabakus, Ismail M.
Afiliación
  • Smith C; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: smitcart@musc.edu.
  • Nance S; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: nances@musc.edu.
  • Chamberlin JH; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: chamberj@musc.edu.
  • Maisuria D; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: maisuria@musc.edu.
  • O'Doherty J; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America; Siemens Healthineers, 40 Liberty Boulevard, Malvern, PA 19355, United States of
  • Baruah D; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: baruah@musc.edu.
  • Schoepf UJ; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: schoepf@musc.edu.
  • Szemes AV; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: vargaasz@musc.edu.
  • Elojeimy S; Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: elojeim@musc.edu.
  • Kabakus IM; Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: kabakus@musc.edu.
Clin Imaging ; 100: 24-29, 2023 Aug.
Article en En | MEDLINE | ID: mdl-37167806
ABSTRACT
RATIONALE Single-photon-emission-computerized-tomography/computed-tomography(SPECT/CT) is commonly used for pulmonary disease. Scant work has been done to determine ability of AI for secondary findings using low-dose-CT(LDCT) attenuation correction series of SPECT/CT.

METHODS:

120 patients with ventilation-perfusion-SPECT/CT from 9/1/21-5/1/22 were included in this retrospective study. AI-RAD companion(VA10A,Siemens-Healthineers, Erlangen, Germany), an ensemble of deep-convolutional-neural-networks was evaluated for the detection of pulmonary nodules, coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss. Accuracy, sensitivity, specificity was measured for the outcomes. Inter-rater reliability were measured. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC) by comparing the number of nodules identified by the AI to radiologist.

RESULTS:

Overall per-nodule accuracy, sensitivity, and specificity for detection of lung nodules were 0.678(95%CI 0.615-0.732), 0.956(95%CI 0.900-0.985), and 0.456(95%CI 0.376-0.543), respectively, with an intraclass correlation coefficient (ICC) between AI and radiologist of 0.78(95%CI 0.71-0.83). Overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.939(95%CI 0.878-0.975), 0.974(95%CI 0.925-0.995), and 0.857(95%CI 0.781-0.915), respectively. Sensitivity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.898(95%CI 0.778-0.966), 1 (95%CI 0.958-1), and 1 (95%CI 0.961-1), respectively. Specificity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.969(95% CI 0.893-0.996), 0.897 (95% CI 0.726-0.978), and 0.346 (95% CI 0.172-0.557), respectively.

CONCLUSION:

AI ensemble was accurate for coronary artery calcium and aortic ectasia/aneurysm, while sensitive for aortic ectasia/aneurysm, lung nodules and vertebral height loss on LDCT attenuation correction series of SPECT/CT.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Calcio Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Clin Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Calcio Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Clin Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article