Evidence map›Paper›PMID 41890140›Full record

ArticleFrontiers in surgery2026

A study on the feasibility and initial outcomes of artificial intelligence-assisted preoperative planning for hip revision surgery.

Jiaqing Zhu, Tianwei Xia, Shanbin Zheng, Lu Wang, Dawei Huan, Xindong Yin, Yong Ma, Jirong Shen

Abstract read
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Article in Frontiers in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jiaqing Zhu *Department of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Tianwei Xia *Department of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Shanbin Zheng *Department of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Lu Wang *Department of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Dawei Huan *Department of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Xindong YinDepartment of General Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Yong MaDepartment of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Jirong ShenDepartment of Orthopaedics and Traumatology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this study was to explore the early and midterm efficacy of artificial intelligence (AI) involvement in assisted hip revision surgery and to summarize our clinical experience. Methods: Clinical data were collected from 38 patients (39 hips) who underwent hip revision surgery and preoperative planning using AI technology in our hospital between June 2019 and November 2024. The cohort included 17 men and 22 women, with a mean age of 70.33 ± 10.42 years (range 44-90 years). Mean follow-up time was 39.67 ± 16.76 months (range 7-72 months). The initial revision was performed in 37 hips, second revision in three hips, and third revision in one hip. The function of the affected limb was evaluated using Harris Hip Scoring System (HHS) at preoperative and postoperative nodes. Results: In this study, 38 patients were monitored for 7-72 months, with a mean follow-up of 34.64 ± 16.54 months. Matching of AI-planned prosthesis size with actual prosthesis size replaced during surgery: 3 cases (7.69%) showed a ±1 size discrepancy, 1 case (2.56%) showed a ±2 size discrepancy, and the remaining patients achieved precise matching. The HHS scores of all patients at 6 months postoperatively (82.99 ± 6.91) were higher than the preoperative scores (33.03 ± 7.36), and the difference was statistically significant ( Conclusion: AI technology makes complex revision surgeries simple by accurately formulating individual preoperative plans for hip revision surgery. The feasibility and preliminary efficacy of AI-assisted revision total hip arthroplasty surgery are satisfactory. AI-assisted complex orthopedic surgeries warrant further in-depth clinical research.

Indexed as

artificial intelligencebone defectspreoperative planningrevision surgerytotal hip arthroplasty

Identifiers

PMID41890140
PMCPMC13013534

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