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  • Arvidsson, IdaLund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,Lund Univ, Sweden (author)

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • 2022-05-24
  • Springer Science and Business Media LLC,2023

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:e2236d6a-ef8c-4075-b0bd-864c99f68cec
  • https://lup.lub.lu.se/record/e2236d6a-ef8c-4075-b0bd-864c99f68cecURI
  • https://doi.org/10.1007/s12350-022-02995-6DOI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-185595URI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:art swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • Funding Agencies|Analytic Imaging Diagnostics Arena, Vinnova Grant [2017-02447]; Department of Clinical Physiology; Department of Radiology, Region Ostergotland
  • Purpose: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning. Methods: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well. Results: Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%). Conclusion: Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.

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  • Davidsson, AnetteLinköpings universitet,Linköping University,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Fysiologiska kliniken US(Swepub:liu)aneda83 (author)
  • Overgaard, Niels ChristianLund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Partiella differentialekvationer,Teknisk matematik (CI),Utbildningsprogram, LTH,Lunds Tekniska Högskola,Matematik LTH,Matematikcentrum,Institutioner vid LTH,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,Lund University Research Groups,Partial differential equations,Engineering Mathematics (M.Sc.Eng.),Educational programmes, LTH,Faculty of Engineering, LTH,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH,Lund Univ, Sweden(Swepub:lu)math-nov (author)
  • Pagonis, ChristosLinköpings universitet,Linköping University,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Kardiologiska kliniken US(Swepub:liu)chrpa43 (author)
  • Åström, KalleLund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Stroke Imaging Research group,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH,Lund Univ, Sweden(Swepub:lu)math-kas (author)
  • Good, ElinLinköpings universitet,Linköping University,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Kardiologiska kliniken US(Swepub:liu)eligo36 (author)
  • Frias-Rose, JeronimoLinköpings universitet,Linköping University,Institutionen för hälsa, medicin och vård,Medicinska fakulteten,Region Östergötland, Klinisk patologi(Swepub:liu)n/a (author)
  • Heyden, AndersLund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH,Lund Univ, Sweden(Swepub:lu)math-ahe (author)
  • Ochoa-Figueroa, MiguelLinköpings universitet,Linköping University,Institutionen för hälsa, medicin och vård,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Fysiologiska kliniken US,Region Östergötland, Röntgenkliniken i Linköping(Swepub:liu)migoc11 (author)
  • Matematik LTHMatematikcentrum (creator_code:org_t)

Related titles

  • In:Journal of Nuclear Cardiology: Springer Science and Business Media LLC30:1, s. 116-1261071-35811532-6551

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