Evidence map›Paper›PMID 38410183›Full record

ArticleIEEE journal of translational engineering in health and medicine2024

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy.

Andrea Moglia, Luca Marsilio, Matteo Rossi, Maria Pinelli, Emanuele Lettieri, Luca Mainardi, Alfonso Manzotti, Pietro Cerveri

Abstract read
In one paragraph

Article in IEEE journal of translational engineering in health and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Andrea MogliaDepartment of ElectronicsInformation and BioengineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0000-0002-3365-580X
Luca MarsilioDepartment of ElectronicsInformation and BioengineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0009-0001-9738-0438
Matteo RossiDepartment of ElectronicsInformation and BioengineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0000-0003-2519-0720
Maria PinelliDepartment of Management, Economics and Industrial EngineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0000-0002-3993-0585
Emanuele LettieriDepartment of Management, Economics and Industrial EngineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0000-0003-1235-989X
Luca MainardiDepartment of ElectronicsInformation and BioengineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0000-0002-6276-6314
Alfonso ManzottiHospital ASST FBF-Sacco 20157 Milan Italy.ORCID 0000-0003-1791-6800
Pietro CerveriDepartment of ElectronicsInformation and BioengineeringPolitecnico di Milano 20133 Milan Italy.ORCID 0000-0003-3995-8673

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveRecent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset.

methodsTwo preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability.

resultsDuring the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks ("Patient selection" and "Scrolling through radiograph") with respect to the second attempt, but without statistically significant difference (respectively [Formula: see text] = 0.14 and [Formula: see text] = 0.13, [Formula: see text]). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. DISCUSSION/

conclusionIn this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.

Indexed as

Augmented RealitySurgery, Computer-AssistedArtificial IntelligenceHumansKnee JointOsteotomydeep neural networksimage segmentationLower limb osteotomysurgical planning

Identifiers

PMID38410183
PMCPMC10896423

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.