ReviewDiagnostics (Basel, Switzerland)2022
A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Performance of artificial intelligence in automated measurement of patellofemoral joint parameters: a systematic review.Journal of orthopaedic surgery and research · 2025Pooled it
- Potential of AI-based diagnostic grading system for knee osteoarthritis.Frontiers in medicine · 2025Article
- Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning.IEEE open journal of engineering in medicine and biology · 2025Article
- Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.Knee surgery & related research · 2024Article
- A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.Quantitative imaging in medicine and surgery · 2024Article
- Survival analysis on subchondral bone length for total knee replacement.Skeletal radiology · 2024Article
- SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images.Medical & biological engineering & computing · 2024Article
- A method for comparing MRI sequences of the knee for segmentation based on morphological features.PloS one · 2024Article
- Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning.Scientific reports · 2023Article
- Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022.Osteoarthritis imaging · 2023Article
- An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- Auto-segmentation of the tibia and femur from knee MR images via deep learning and its application to cartilage strain and recovery.Journal of biomechanics · 2023Article
- Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images.Sensors (Basel, Switzerland) · 2022Article
- Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network.Life (Basel, Switzerland) · 2022Article
- Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images.Diagnostics (Basel, Switzerland) · 2022Article
- Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. The majority of KOA is primarily based on hyaline cartilage change, according to medical images. However, technical bottlenecks such as noise, artifacts, and modality pose enormous challenges for an objective and efficient early diagnosis. Therefore, the correct prediction of arthritis is an essential step for effective diagnosis and the prevention of acute arthritis, where early diagnosis and treatment can assist to reduce the progression of KOA. However, predicting the development of KOA is a difficult and urgent problem that, if addressed, could accelerate the development of disease-modifying drugs, in turn helping to avoid millions of total joint replacement procedures each year. In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization. In this paper, we seek to give an in-depth understanding of a wide range of the most recent methodologies for knee articular bone segmentation; segmentation methods allow the estimation of articular cartilage loss rate, which is utilized in clinical practice for assessing the disease progression and morphological change, ranging from traditional techniques to deep learning (DL)-based techniques. Moreover, the purpose of this work is to give researchers a general review of the currently available methodologies in the area. Therefore, it will help researchers who want to conduct research in the field of KOA, as well as highlight deficiencies and potential considerations in application in clinical practice. Finally, we highlight the diagnostic value of deep learning for future computer-aided diagnostic applications to complete this review.
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.