Evidence map›Paper›PMID 39043748›Full record

ArticleScientific reports2024

Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty.

Qing Lan, Shulin Li, Jiahao Zhang, Huiling Guo, Laipeng Yan, Faqiang Tang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

6 authors.

Qing Lan *Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Shulin Li *Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Jiahao Zhang *Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Huiling GuoShengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Laipeng YanShengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Faqiang TangShengli Clinical Medical College of Fujian Medical University, Fuzhou, China. faqiangtang@fjmu.edu.cn.

Funding

Natural Science Foundation of Fujian Province 2022J01401
6 · The paper itself

Abstract

The size and axial alignment of prostheses, when planned during total knee replacement (TKA) are critical for recovery of knee function and improvement of knee pain symptoms. This research aims to study the effect of artificial intelligence (AI)-based preoperative three dimensional (3D) planning technology on prosthesis size and axial alignment planning in TKA, and to compare its advantages with two dimensional (2D) X-ray template measurement technology. A total of 60 patients with knee osteoarthritis (KOA) who underwent TKA for the first time were included in the AI (n = 30) and 2D (n = 30) groups. The preoperative and postoperative prosthesis size, femoral valgus correction angle (VCA) and hip-knee-ankle angle (HKA) were recorded and compared between the two groups. The results of the University of Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and the American Knee Association Score (AKS) were evaluated before surgery, 3 months, 6 months, and 12 months after surgery. The accuracy of prosthesis size, VCA and HKA prediction in AI group was significantly higher than that in 2D group (P < 0.05). The WOMAC and AKS scores in AI group at 3 months, 6 months and 12 months after surgery were better than those in 2D group (P < 0.05). Both groups showed significant improvement in WOMAC and AKS scores at 12 months follow-up. AI-based preoperative 3D planning technique has more reliable planning effect for prosthesis size and axial alignment in TKA.

Indexed as

Arthroplasty, Replacement, KneeArtificial IntelligenceKnee ProsthesisOsteoarthritis, KneeAgedFemaleHumansImaging, Three-DimensionalKnee JointMaleMiddle AgedPreoperative CarePreoperative Period

Identifiers

PMID39043748
PMCPMC11266554

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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.