Evidence map›Paper›PMID 35489006›Full record

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.

Haitao Li, Jiangchang Xu, Dingzhong Zhang, Yaohua He, Xiaojun Chen

Abstract read
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In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
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

3 citing papers in PubMed, 8 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Haitao Li *Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room A-925, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
Jiangchang Xu *Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room A-925, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.ORCID http://orcid.org/0000-0003-3187-888X
Dingzhong ZhangInstitute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room A-925, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
Yaohua HeDepartment of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai, 200233, China. heyaohua@sjtu.edu.cn.
Xiaojun ChenInstitute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room A-925, Dongchuan Road 800, Minhang District, Shanghai, 200240, China. xiaojunchen@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-0298-4491
Shanghai Jiao Tong University · CN

Funding

Academician Expert Workstation of the Jinshan District jszjz2020007YClinical Research Center of Shanghai University of Medicine & Health Sciences 20MC2020003Foundation of Science and Technology Commission of Shanghai Municipality 19510712200Foundation of Science and Technology Commission of Shanghai Municipality 20490740700National Natural Science Foundation of China 81871755National Natural Science Foundation of China 81971709National Natural Science Foundation of China 82011530141National Natural Science Foundation of China 82072401National Natural Science Foundation of China M-0019Plan of Medical Key Specialty Construction, and the Shanghai Health Committee ZK2019B03Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research YG2019ZDA06Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research YG2021QN72Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research YG2021ZD21Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research YG2022QN056the Program of Shanghai Academic/Technology Research Leader 19XD1402800the Shanghai Municipal Education Commission Scientific Research and Innovation Program 2021-01-07-00-02-E00082
6 · The paper itself

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

Arthroplasty, Replacement, ShoulderShoulder JointBone DensityBone ScrewsHumansTomography, X-Ray ComputedAutomatic surgical planningProsthesis implantationReverse shoulder arthroplastyShoulder joint diseases

Identifiers

PMID35489006
OpenAlexW4225123172

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.