To qualitatively assess the clinical usefulness of patient-specific high-fidelity three-dimensional (3D) print model of kidney before partial nephrectomy (PN) and to identify subset domains where it may help in clinical terms. Thirteen 3D models were printed for tumors having RENAL nephrometry score of ≥8. Their usage for PN was assessed prospectively using a qualitative questionnaire to be answered on a Likert scale of 1-10. The questions focused on realistic resemblance, preoperative dry surgical run, intertest comparison, surgical impact, and overall beneficence domains as perceived by primary surgeons with respect to surgical conduct during PN. Mean RENAL score was 9.15 (8-11). Models were rated high (9.07 ± 0.86) for realistic resemblance domain and were rated better than contrast-enhanced computed tomography (CECT) (8.38 ± 0.87) and intraoperative ultrasonography (8.07 ± 1.26) for orientation regarding resection margins. A further marginal improvement to 8.2 ± 0.84 was noted against ultrasound where surgeon did a dry cut preoperatively. Use of superselective arterial approach in four, precise awareness about dissection of a major vessel in four, retroperitoneoscopic approach in one, and surgical margin awareness in three were directly attributed to the model. Overall utility of having a model printed was rated high (8.23 ± 1.3). The 3D print models of complex renal tumors have high realistic resemblance to actual patient's anatomy. They were rated better than preoperative CECT or intraoperative ultrasonography for orientation regarding surgical resection margins. It may also help change or modify the surgical plan in a subset of patients with a potential to improve overall outcomes in these complex cases.
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http://dx.doi.org/10.1089/end.2020.1103 | DOI Listing |
Prog Neurobiol
January 2025
Section on Cognitive Neurophysiology and Imaging, Systems Neurodevelopment Laboratory, National Institute of Mental Health, Bethesda, MD, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD, USA. Electronic address:
The macaque cerebral cortex contains concentrations of neurons that prefer faces over inanimate objects. Although these so-called face patches are thought to be specialized for the analysis of facial signals, their exact tuning properties remain unclear. For example, what happens when an object by chance resembles a face? Everyday objects can sometimes, through the accidental positioning of their internal components, appear as faces.
View Article and Find Full Text PDFFront Bioeng Biotechnol
December 2024
Department of Neurosurgery, University Hospital Leipzig, Leipzig, Germany.
Comput Methods Programs Biomed
December 2024
Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore. Electronic address:
Background: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicating statistical properties of real data while enhancing privacy protection.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Faculty of Electrical Engineering and Informatics, University of Pardubice, Pardubice, Czechia.
A novel methodology for dataset augmentation in the semantic segmentation of coil-coated surface degradation is presented in this study. Deep convolutional generative adversarial networks (DCGAN) are employed to generate synthetic input-target pairs, which closely resemble real-world data, with the goal of expanding an existing dataset. These augmented datasets are used to train two state-of-the-art models, U-net, and DeepLabV3, for the precise detection of degradation areas around scribes.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
State Key Laboratory of Fine Chemicals, Dalian R&D Center for Stem Cell and Tissue Engineering, Dalian University of Technology, Dalian 116024, China; Zhengzhou Institute of Emerging Industrial Technology, Zhengzhou 450000, China; Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110042, China. Electronic address:
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