Robotic-assisted closure of atrial septal defect under real-time three-dimensional echo guide: in vitro study
Bibliographic record
Abstract
BACKGROUND: Several advances in robotic technology and imaging systems have enabled the broad application of minimally invasive techniques in cardiac surgery. We have previously demonstrated that real-time three-dimensional echocardiography (RT3DE) provided adequate imaging and anatomic detail to act as a sole guide for surgical task performance. In this study, we examined the feasibility of robotic-assisted RT3DE-guided repair of atrial septal defect (ASD) in an in vitro study. MATERIALS AND METHODS: Exp. I: An RT3DE system with x4 matrix transducer (Sonos 7500, Philips Medical Systems, Andover, MA) was compared to two-dimensional echo (2DE) in the performance of common surgical tasks with the da Vinci Robotic Surgical System (Intuitive Surgical, Sunnyvale, CA). Completion times and deviation of suture from an echogenic target (mm) were measured. Exp. II: Porcine ASDs (n=10) were created and closed with robotic-assisted direct suturing in a water bath. During all experiments the operator was blinded to the target and operated only under ultrasonic guidance. RESULTS: Compared to 2DE guidance, completion times improved by 70% (p<0.0001) and deviation of suture by the robotic system was significantly smaller (2DE: 4+/-2mm, 3DE: 0.2+/-0.3mm, p=0.0002) in RT3DE-guided tasks. RT3DE provided satisfactory images and sufficient anatomical detail for suturing. All surgical tasks were successfully performed with accuracy. CONCLUSIONS: These initial experiments demonstrate the feasibility of robotic-assisted direct closure of ASD under RT3DE guidance. An endoscopic port access approach may be possible with refinements in telemanipulator technology and further development of the transesophageal echo transducer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".