Evidence map›Paper›PMID 37633032›Full record

ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2023

CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.

Amir Rouhollahi, James Noel Willi, Sandra Haltmeier, Alireza Mehrtash, Ross Straughan, Hoda Javadikasgari, Jonathan Brown, Akinobu Itoh, Kim I de la Cruz, Elena Aikawa and 2 more

Abstract read
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In one paragraph

Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Perception-first digital twin for augmented reality microsurgery.International journal of computer assisted radiology and surgery · 2026
    Article
  3. AortaExplorer: AI-driven analysis of the aorta in CT images.Medical & biological engineering & computing · 2026
    Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025
    Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Amir RouhollahiDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
James Noel WilliDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Sandra HaltmeierDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Alireza MehrtashDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Ross StraughanDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
Hoda JavadikasgariDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Jonathan BrownClinical and Translation Science Institute, Tufts University, Boston, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Akinobu ItohDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Kim I de la CruzDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Elena AikawaCenter for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Center for Excellence in Vascular Biology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Elazer R EdelmanInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Farhad R NezamiDivision of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: frikhtegarnezami@bwh.harvard.edu.

Funding

Personalized lesion modification optimizes atherosclerosis interventionR01HL161069 · NHLBI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI EDELMAN, ELAZER R · 2022 to 2025
$2.8M
NHLBI NIH HHS R01 HL161069
6 · The paper itself

Abstract

Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the prevalence of AS is projected to rise, resulting in a progressively significant healthcare and economic burden. While surgical aortic valve replacement (SAVR) has been the gold standard approach, the less invasive transcatheter aortic valve replacement (TAVR) is poised to become the dominant method for high- and medium-risk interventions. Computational simulations using patient-specific models, have opened new research avenues for optimizing emerging devices and predicting clinical outcomes. The traditional techniques of generating digital replicas of patients' aortic root, native valve, and calcification are time-consuming and labor-intensive processes requiring specialized tools and expertise in anatomy. Alternatively, deep learning models, such as the U-Net architecture, have emerged as reliable and fully automated methods for medical image segmentation. Two-dimensional U-Nets have been shown to produce comparable or more accurate results than trained clinicians' manual segmentation while significantly reducing computational costs. In this study, we have developed a fully automatic AI tool capable of reconstructing the digital twin geometry and analyzing the calcification distribution on the aortic valve. The developed automatic segmentation package enables the modeling of patient-specific anatomies, which can then be used to simulate virtual interventional procedures, optimize emerging prosthetic devices, and predict clinical outcomes.

Indexed as

Aortic Valve StenosisDeep LearningHeart Valve Prosthesis ImplantationTranscatheter Aortic Valve ReplacementAgedAortic ValveHumansRisk FactorsTreatment OutcomeAortic stenosisAutomated AI platformCalcium distributionDigital twin

Identifiers

PMID37633032
PMCPMC10599298

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.