ArticleInternational journal of computer assisted radiology and surgery2022
Automatic surgical planning based on bone density assessment and path integral in cone space for reverse shoulder arthroplasty.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed, 8 citations in OpenAlex.
- An End-to-End Geometry-Based Pipeline for Automatic Preoperative Surgical Planning of Pelvic Fracture Reduction and Fixation.IEEE transactions on medical imaging · 2025Article
- The development of a novel navigation system for reverse shoulder arthroplasty and its accuracy: a phantom and cadaveric study.International journal of computer assisted radiology and surgery · 2024Article
- Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeReverse shoulder arthroplasty (RSA) is an effective surgery for severe shoulder joint diseases. Traditionally, the preoperative planning procedure of RSA is manually conducted by experienced surgeons, resulting in prolonged operating time and unreliable drilling paths of the prosthetic fixation screws. In this study, an automatic surgical planning algorithm for RSA was proposed to compute the optimal path of screw implantation.
methodsFirstly, a cone-shaped space containing alternative paths for each screw is generated using geometric parameters. Then, the volume constraint is applied to automatically remove inappropriate paths outside the bone boundary. Subsequently, the integral of grayscale value of the CT is used to evaluate the bone density and to compute the optimal solution. An automatic surgical planning software for RSA was also developed with the aforementioned algorithms.
resultsTwenty-four clinical cases were used for preoperative planning to evaluate the accuracy and efficiency of the system. Results demonstrated that the angles among the prosthetic fixation screws were all within constraint angle(45°), and the stability rate of the planned prosthesis was 94.92%. The average time for the automatic planning algorithm was 4.39 s, and 83.96 s for the whole procedure. Repetitive experiments were also conducted to demonstrate the robustness of our system, and the variance of the stability coefficient was 0.027%.
conclusionsIn contrast to the cumbersome manual planning of the existing methods for RSA, our method requires only simple interaction operations. It enables efficient and precise automatic preoperative planning to simulate the ideal placement of the long prosthetic screws for the long-term stability of the prosthesis. In the future, it will have great clinical application prospects in RSA.
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.