Image-guided techniques in renal and hepatic interventions
Bibliographic record
Abstract
BACKGROUND: Development of new imaging technologies and advances in computing power have enabled the physicians to perform medical interventions on the basis of high-quality 3D and/or 4D visualization of the patient's organs. Preoperative imaging has been used for planning the surgery, whereas intraoperative imaging has been widely employed to provide visual feedback to a clinician when he or she is performing the procedure. In the past decade, such systems demonstrated great potential in image-guided minimally invasive procedures on different organs, such as brain, heart, liver and kidneys. This article focuses on image-guided interventions and surgery in renal and hepatic surgeries. METHODS: A comprehensive search of existing electronic databases was completed for the period of 2000-2011. Each contribution was assessed by the authors for relevance and inclusion. The contributions were categorized on the basis of the type of operation/intervention, imaging modality and specific techniques such as image fusion and augmented reality, and organ motion tracking. RESULTS: As a result, detailed classification and comparative study of various contributions in image-guided renal and hepatic interventions are provided. In addition, the potential future directions have been sketched. CONCLUSION: With a detailed review of the literature, potential future trends in development of image-guided abdominal interventions are identified, namely, growing use of image fusion and augmented reality, computer-assisted and/or robot-assisted interventions, development of more accurate registration and navigation techniques, and growing applications of intraoperative magnetic resonance imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".