ArticleMedical & biological engineering & computing2018
Dynamic ensemble selection of learner-descriptor classifiers to assess curve types in adolescent idiopathic scoliosis.
Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- An Innovative Stepwise C-Means Clustering Approach for Classification of Adolescent Idiopathic Scoliosis.Orthopaedic surgery · 2025Article
- 2D and 3D Classification Systems for Adolescent Idiopathic Scoliosis: Clinical Implications and Technological Advances.Orthopaedic surgery · 2025Review
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- Current issues in the treatment of adolescent idiopathic scoliosis: a comprehensive narrative review.Asian spine journal · 2024Article
- Accuracy of 3D printed spine models for pre-surgical planning of complex adolescent idiopathic scoliosis (AIS) in spinal surgeries: a case series.Annals of 3D printed medicine · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- A narrative review of machine learning as promising revolution in clinical practice of scoliosis.Annals of translational medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
While classification is important for assessing adolescent idiopathic scoliosis (AIS), it however suffers from low interobserver and intraobserver reliability. Classification using ensemble methods may contribute to improving reliability using the proper 2D and 3D images of spine curvature features. In this study, we present two new techniques to describe the spine, namely, leave-one-out and fan leave-one-out. Using these techniques, three descriptors are computed from a stereoradiographic 3D reconstruction to describe the relationship between a vertebra and its neighbors. A dynamic ensemble selection method is introduced for automatic spine classification. The performance of the method is evaluated on a dataset containing 962 3D spine models categorized according to three curve types. With a log loss of 0.5623, the dynamic ensemble selection outperforms voting and stacking ensemble learning techniques. This method can improve intraobserver and interobserver reliability, identify the best combination of descriptors for characterizing spine curve types, and provide assistance to clinicians in the form of information to classify borderline curvature types. Graphical abstract ᅟ.
Indexed as
Identifiers
29949021What 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.