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Rebuilding the Knee: From Repair to Replacement and Recovery
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Rebuilding the Knee: From Repair to Replacement and Recovery

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Orthopedics".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 220

Editor

Dr. Young-Jin Seo

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
Interests: ligament reconstruction; cartilage regeneration; meniscus preservation; patellofemoral disorders; osteotomy; joint preservation; knee arthroplasty; functional recovery; knee
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The knee has evolved from being viewed as a simple mechanical joint to a complex organ requiring integrated strategies to restore its stability, alignment, and biologic function, and to facilitate patient recovery. This Special Issue, Rebuilding the Knee: From Repair to Replacement and Recovery, aims to highlight contemporary advances across the full spectrum of knee surgery and rehabilitation. Topics of interest include ligament reconstruction, meniscus repair and transplantation, cartilage regeneration, treatment of patellofemoral disorders, corrective osteotomy, and partial or total knee arthroplasty.

In recent years, rapid developments in surgical techniques, biologic augmentation, imaging technologies, and rehabilitation protocols have transformed knee disorder management. Alongside technical innovation, increasing emphasis has been placed on individualized treatment strategies, functional recovery, return to sports and daily activities, and long-term joint preservation. This Issue seeks to bridge basic science, surgical practice, and postoperative rehabilitation to provide a comprehensive perspective on modern knee care.

We welcome the submission of reviews and original research articles that contribute to improving clinical outcomes and long-term joint health across diverse patient populations. Particular attention will be given to studies addressing patient-centered outcomes, biomechanical restoration, biologic healing, and evidence-based rehabilitation strategies.

Dr. Young-Jin Seo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cartilage
  • meniscus
  • total knee arthroplasty
  • knee surgery
  • anterior cruciate ligament injuries
  • posterior cruciate ligament injuries
  • ligament reconstruction
  • patellofemoral disorders
  • osteotomy
  • sports medicine
  • rehabilitation
  • functional recovery

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Published Papers (1 paper)

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Research

29 pages, 2425 KB  
Article
Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Multi-Stage CNN Framework with External Validation
by Nitiphoom Sinnathakorn, Chanon Fahpinyo, Watcharaporn Cholamjiak and Suthep Suantai
J. Clin. Med. 2026, 15(13), 5222; https://doi.org/10.3390/jcm15135222 - 3 Jul 2026
Viewed by 86
Abstract
Background/Objectives: Osteoporosis is a major public health concern associated with increased fracture risk and reduced quality of life if not detected at an early stage. Automated analysis of knee X-ray images using artificial intelligence has shown promising potential for opportunistic osteoporosis screening. This [...] Read more.
Background/Objectives: Osteoporosis is a major public health concern associated with increased fracture risk and reduced quality of life if not detected at an early stage. Automated analysis of knee X-ray images using artificial intelligence has shown promising potential for opportunistic osteoporosis screening. This study aims to develop and evaluate a multi-stage deep learning and machine learning framework for osteoporosis classification, with particular emphasis on external validation, calibration drift, and cross-domain generalization performance. Methods: Knee X-ray images were categorized into three classes: Normal, Osteopenia, and Osteoporosis. Deep features were extracted using pretrained convolutional neural networks, including ResNet18, EfficientNetB0, and DenseNet121. The extracted features were subsequently classified using multiple machine learning models, including Neural Network, Efficient Linear, Support Vector Machine, and Naive Bayes classifiers. Two data augmentation strategies were investigated: targeted minority-class augmentation and full 3× dataset expansion with class balancing. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC on internal validation, independent test sets, and external validation datasets. Additional analyses included reliability calibration assessment, isotonic recalibration, and class-prior boosting with cross-validated threshold optimization to address external domain shift. Results: EfficientNetB0 and DenseNet121 consistently outperformed ResNet18 across most evaluation metrics. Under the balanced augmentation strategy, EfficientNetB0 combined with Efficient Linear demonstrated strong and stable performance, while DenseNet121 paired with a Neural Network achieved the highest overall classification performance. External validation revealed a substantial discrepancy between AUC and threshold-based metrics, indicating the presence of calibration drift and class-prior mismatch across imaging domains. Reliability analysis showed severe probability collapse in the Osteopenia class during external testing. Post-hoc recalibration improved probability reliability, while class-prior boosting substantially increased Osteopenia sensitivity and improved balanced accuracy and macro F1-score under external validation conditions. Conclusions: The proposed framework demonstrates the feasibility of combining pretrained CNN-based deep feature extraction with machine learning classifiers for osteoporosis classification from knee X-ray images. The findings further highlight that maintaining model performance under external testing conditions may require not only strong feature extraction capability but also adaptive recalibration and deployment-aware threshold optimization to address calibration drift and cross-domain variability. While the results are encouraging, the present study should be considered a proof-of-concept investigation. Although the framework was evaluated using an independent public external dataset, further validation using larger and more diverse multi-center clinical cohorts is necessary to establish generalizability and clinical utility before routine clinical implementation can be considered. Full article
(This article belongs to the Special Issue Rebuilding the Knee: From Repair to Replacement and Recovery)
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