ArticleWorld journal of orthopedics2023
Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs.
Article in World journal of orthopedics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Hallux valgus: Advances in weightbearing CT imaging and three-dimensional assessment of first ray deformity.Skeletal radiology · 2026Review
- Development and Application of Automated Preoperative Planning System for Developmental Dysplasia of the Hip via Keypoint Selective Integration.Journal of imaging informatics in medicine · 2026Article
- Evaluation of surgical recommendation algorithms in hallux valgus cases using ChatGPT models: an artificial intelligence approach based on 50 simulated scenarios.Acta orthopaedica et traumatologica turcica · 2026Article
- Automated measurement of hallux valgus angles using a deep learning model: validation and comparison with surgeons of varying expertise.Scientific reports · 2026Article
- Deep learning versus manual measurement of hallux valgus angle and intermetatarsal angle on Weight-Bearing X-rays in hallux valgus.BMC musculoskeletal disorders · 2025Article
- AI-Powered Smartphone Application for Measuring Hallux Valgus Angle From Radiographs Displayed on a Monitor.Journal of foot and ankle research · 2025Article
- Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques.BMC medical imaging · 2025Article
- Artificial intelligence-based diagnosis of hallux valgus interphalangeus using anteroposterior foot radiographs.World journal of orthopedics · 2025Article
- Utility of Weight-Bearing Computed Tomography in the Postoperative Assessment of Ankle Fractures.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning automation of radiographic patterns for hallux valgus diagnosis.World journal of orthopedics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAssessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.
aimTo investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.
methodsThe data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs. 181 radiographs were utilized for training (161) and validating (20) a U-Net neural network to achieve a mean Sørensen-Dice index > 97% on bone segmentation. 84 test radiographs were used for manual (computer assisted) and automated measurements of hallux valgus severity determined by hallux valgus (HVA) and intermetatarsal angles (IMA). The reliability of manual and computer-based measurements was calculated using the interclass correlation coefficient (ICC) and standard error of measurement (SEM). Inter- and intraobserver reliability coefficients were also compared. An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.
resultsVery high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians. For HVA, the ICC between manual measurements was 0.96-0.99. For IMA, ICC was 0.78-0.95. Comparing manual against automated computer measurement, the reliability was high as well. For HVA, absolute agreement ICC and consistency ICC were 0.97, and SEM was 0.32. For IMA, absolute agreement ICC was 0.75, consistency ICC was 0.89, and SEM was 0.21. Additionally, a strong correlation (0.80) was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus, according to an operative treatment algorithm proposed by EFORT.
conclusionThe proposed automated, artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool, with comparable accuracy to manual measurements by expert clinicians. Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs, classify the deformity severity, streamline preoperative decision-making prior to corrective surgery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.