Evidence map›Paper›PMID 40011609›Full record

ArticleScientific reports2025

Combining an improved political optimizer with convolutional neural networks for accurate anterior cruciate ligament tear detection in sports injuries.

Wei Hu, Saeid Razmjooy

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Wei HuSchool of Physical Education, Chongqing Technology and Business University, Chongqing, 400067, China. 13983175707@163.com.
Saeid RazmjooyDepartment of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran. razmjooysaeid@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new technique has been developed to identify ACL tears in sports injuries. This method utilizes a Convolutional Neural Network (CNN) in combination with a modified Political Optimizer (IPO) algorithm, resulting in a major breakthrough in detecting ACL tears. The study provides an innovative approach to detecting this type of injury. The CNN/IPO approach surpasses traditional optimization techniques, ensuring precise and timely detection of ACL tears. This breakthrough has the potential to significantly improve treatment results, enabling clinicians to intervene promptly and effectively, leading to enhanced recovery and rehabilitation for athletes. The integration of the CNN and IPO algorithm provides clinicians with an unparalleled level of accuracy and efficiency in identifying ACL tears, facilitating more precise and tailored treatment strategies for sports-related injuries. The findings have the potential to revolutionize the way medical professionals approach musculoskeletal injuries, enhancing overall well-being and athletic performance. The research's significance extends beyond sports medicine, illuminating new avenues for the detection and management of ACL tears, and paving the way for advancements in sports injury diagnosis and treatment.

Indexed as

Anterior Cruciate Ligament InjuriesAthletic InjuriesNeural Networks, ComputerAlgorithmsAnterior Cruciate LigamentConvolutional Neural NetworksFemaleHumansMaleAnterior cruciate ligament tearClinical imagesConvolutional neural networkDetectionImproved versionOptimization techniquesPolitical optimizerSports injuries

Identifiers

PMID40011609
PMCPMC11865563

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.