The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predictive Medicine: Computational Techniques in Therapeutic Decision-Making
Open accessAbstract
The current paradigm for surgery planning for the treatment of cardiovascular disease relies exclusively on diagnostic imaging data to define the present state of the patient, empirical data to evaluate the efficacy of prior treatments for similar patients, and the judgement of the surgeon to decide on a preferred treatment. The individual variability and inherent complexity of human biological systems is such that diagnostic imaging and empirical data alone are insufficient to predict the outcome of a given treatment for an individual patient. We propose a new paradigm of predictive medicine in which the physician utilizes computational tools to construct and evaluate a combined anatomidphysiologic model to predict the outcome of alternative treatment plans for an individual patient. The predictive medicine paradigm is implemented in a software system developed for Simulation-Based Medical Planning. This system provides an integrated set of tools to test hypotheses regarding the effect of alternate treatment plans on blood flow in the cardiovascular system of an individual patient. It combines an internet-based user interface developed using Java and VRML, image segmentation, geometric solid modeling, automatic finite element mesh generation, computational fluid dynamics, and scientific visualization techniques. This system is applied to the evaluation of alternate, patient-specific treatments for a case of lower extremity occlusive cardiovascular disease.
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Cites
Hughes, T. J. R., Franca, L. P., & Hulbert, G. M. (1989) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/0045-7825(89)90111-4 · Cited by 1,458
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Piccinelli, M., Veneziani, A., Steinman, D. A., Remuzzi, A., & Antiga, L. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2021652 · Cited by 346
Kadem, M., Garber, L., Abdelkhalek, M., Al-Khazraji, B. K., & Keshavarz-Motamed, Z. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3142058 · Cited by 89
Applicability Analysis of Validation Evidence for Biomedical Computational Models
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Metadata
- Authors
- Taylor, C. A., Draney, M. T., Ku, J. P., Parker, D., Steele, B. N., Wang, K., & Zarins, C. K.
- Venue
- Computer Aided Surgery
- Publisher
- Informa UK Limited
- Published
- 1999-01
- Volume
- 4
- Issue
- 5
- Pages
- 231-247
- ISSN
- 1092-9088 (ISSN-L)
- 1097-0150
- OpenAlex
- W4251574976
- Cited by
- 221
- References
- 1
- FWCI
- 6.30
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Taylor, C. A., Draney, M. T., Ku, J. P., Parker, D., Steele, B. N., Wang, K., & Zarins, C. K. (1999). Predictive Medicine: Computational Techniques in Therapeutic Decision-Making. Computer Aided Surgery. https://doi.org/10.3109/10929089909148176
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
