ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2022
A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery.
Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Augmented reality in robotic urology: from 3D printed models to cognitive surgical systems. A systematic literature review.Journal of robotic surgery · 2026Pooled it
- Deep learning-based renal artery segmentation and angle estimation for registration of endoscopic images and 3D models in robot-assisted partial nephrectomy.International journal of computer assisted radiology and surgery · 2026Article
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- From robot assistance to surgical intelligence: global research trends and emerging frontiers of artificial intelligence-enhanced robotic surgery in urology.Journal of robotic surgery · 2026Review
- Artificial intelligence-driven real-time assistance in minimally invasive surgery: a technology-oriented narrative review.Journal of minimally invasive surgery · 2026Review
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- IMAU-Net: A Hybrid Multi-Scale Deep Learning Framework for Liver Segmentation from Laparoscopic Images.Sensors (Basel, Switzerland) · 2026Article
- Human-in-the-loop validation of an artificial intelligence-driven real-time prostatic capsule recognition model for nerve-sparing robot-assisted radical prostatectomy.Surgical endoscopy · 2026Article
- Artificial intelligence analysis of minimally invasive surgery data.Journal of robotic surgery · 2026Review
- Introducing Holographic Surgical Navigation in Pediatric Wilms' Tumor Patients: A Feasibility Study During Total Nephrectomy.Bioengineering (Basel, Switzerland) · 2025Article
- An augmented reality overlay for navigated prostatectomy using fiducial-free 2D-3D registration.International journal of computer assisted radiology and surgery · 2025Article
- Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair.Hernia : the journal of hernias and abdominal wall surgery · 2024Article
- Precision at scale: Machine learning revolutionizing laparoscopic surgery.World journal of clinical oncology · 2024Article
- Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance.Biomedicines · 2024Article
- Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience.Cancers · 2024Article
- Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy.Journal of clinical medicine · 2023Article
- Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2023Review
- A Systematic Review of New Imaging Technologies for Robotic Prostatectomy: From Molecular Imaging to Augmented Reality.Journal of clinical medicine · 2023Review
- Evolution of a surgical system using deep learning in minimally invasive surgery (Review).Biomedical reports · 2023Review
- A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming.Asia-Pacific journal of oncology nursing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe current study presents a deep learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic robot-assisted procedures.
methodsThis framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation.
resultsThe segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. DISCUSSION: Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of deep learning and augmented reality to generalise the automatic registration process.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.