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Machine Learning and Artificial Intelligence Applications in Knee Surgery

Machine Learning and Artificial Intelligence Applications in Knee Surgery

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00040371
Enrollment
40
Registered
2026-05-18
Start date
2026-05-15
Completion date
Unknown
Last updated
2026-06-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Knee cartilage defect

Interventions

Group 1: Retrospective analysis of all patients who underwent cartilage reconstruction of the knee joint between 2014 and 2025.

Sponsors

Vulpius Klinik
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: This retrospective study includes patients who underwent cartilage reconstruction surgery during the period from January 2014 to December 2025.

Exclusion criteria

Exclusion criteria: Minors, insufficient patient records, lack of documentation regarding the measured size of the cartilage defect in surgery, poor quality of arthroscopic images

Design outcomes

Primary

MeasureTime frame
- Cartilage defect sizes are retrospectively measured in arthroscopy images using a machine learnig tool and compared with conventional measurement methods. - The causes of cartilage injuries are described descriptively and presented with frequency distributions. - The type of cartilage treatment procedure (e.g., microfracture, minced cartilage, autologous matrix-induced chondrogenesis, autologous chondrocyte transplantation) is analyzed descriptively and presented with frequency distributions. - After determining the cartilage defect size, treatment recommendations from various large language models are compared with the actual cartilage treatment procedure using inferential statistics.

Secondary

MeasureTime frame
- Radiological findings (e.g., joint space narrowing, concomitant injuries on X-ray and MRI) are summarized descriptively. A correlation between cartilage defect size measured by magnetic resonance imaging (MRI) and arthroscopy (using conventional and new method) is investigated using linear regression analysis if necessary. - The demographic information of the included patients (e.g., age, sex, BMI) is summarized descriptively. The empirical distribution of data and scores is described using mean, standard deviation, minimum, median, and maximum. - Intra- and postoperative complications and any revision surgeries are analyzed using frequency distributions if necessary.

Countries

Germany

Contacts

Public ContactMustafa Canberk Göktepe

Universitätsklinikum Heidelberg, Zentrum für Orthopädie, Unfallchirurgie und Paraplegiologie

MustafaCanberk.Goektepe@med.uni-heidelberg.de+49 62215634329

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Jun 11, 2026