ArticleJournal of healthcare engineering2021
Study on Collision Detection and Force Feedback Algorithm in Virtual Surgery.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Construction of a virtual reality platform for computer-aided navigation Lingnan bone setting technique.Bioengineered · 2023Article
- A virtual surgical prototype system based on gesture recognition for virtual surgical training in maxillofacial surgery.International journal of computer assisted radiology and surgery · 2023Article
- Extended reality for biomedicine.Nature reviews. Methods primers · 2023Article
- Retracted: Study on Collision Detection and Force Feedback Algorithm in Virtual Surgery.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-05-24Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/9D06C101A6460EBC0D0066B5462831
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of virtual reality technology is expected to solve traditional surgical training. The lack of methods has brought revolutionary advances in technology. The virtual surgery system based on collision detection and force feedback can enable the operator to have stronger interaction, which is an exploration of the feature of touch in virtual reality technology. Reality is an important indicator of the virtual surgical system. This article improves the realism of the system from the visual and tactile senses and uses the surrounding ball collision detection and force feedback algorithms to build a realistic surgical platform. In the virtual surgery training system, the introduction of force feedback greatly improves the sense of presence during virtual surgery interaction. The operator can feel the softness and hardness of different tissues and organs through the force feedback device. Virtual reality is an interdisciplinary comprehensive technology that has been widely used in military, film, medical, and gaming fields. Virtual reality can simulate the objective world and display it visually, making people feel immersive. Virtual surgery provides surgeons with a recyclable surgical practice platform and can help doctors perform preoperative rehearsals and predict the results of surgery. The design of collision detection and force feedback algorithms is a prerequisite to ensure the immersion and transparency of the virtual surgical training system. This article mainly introduces the collision detection and force feedback algorithm research in virtual surgery, with the intention of providing some ideas and directions for the development of virtual surgery. This paper proposes two collision detection algorithms, space decomposition method and hierarchical bounding box method, and three force feedback algorithms including spring mass point algorithm, Runge-Kutta method, and Euler method to construct virtual surgery collision detection and force feedback.
Indexed as
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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.