Evidence map›Paper›PMID 42590649›Full record

ArticleSensors (Basel, Switzerland)2026

Ex Vivo Differentiation of Cartilage Degeneration Severity and Subchondral Bone Using Vibroacoustic Signals from Instrument-Tissue Interaction.

Thomas Sühn, Maximilian Costa, Nazila Esmaeili, Moritz Spiller, Axel Boese, Jessica Bertrand, Michael Friebe, Alfredo Illanes, Christoph H Lohmann

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Thomas SühnDepartment of Orthopaedic Surgery, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.ORCID 0000-0001-5132-2884
Maximilian CostaDepartment of Orthopaedic Surgery, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.ORCID 0000-0002-7002-1734
Nazila EsmaeiliCenter for Digital Surgery, Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, 37075 Göttingen, Germany.ORCID 0000-0001-9741-9788
Moritz SpillerSURAG Medical GmbH, 04229 Leipzig, Germany.ORCID 0000-0002-5470-4850
Axel BoeseINKA Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.ORCID 0000-0002-5874-7145
Jessica BertrandDepartment of Orthopaedic Surgery, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.ORCID 0000-0001-9511-4999
Michael FriebeFaculty of Computer Science, AGH University of Krakow, 30-059 Kraków, Poland.ORCID 0000-0002-8624-0800
Alfredo IllanesFaculty of Computer Science, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.ORCID 0000-0002-0118-0483
Christoph H LohmannDepartment of Orthopaedic Surgery, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany.ORCID 0000-0002-2962-2183

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis (OA) of the knee is characterized by cartilage matrix degeneration, which eventually leads to joint dysfunction and surgical replacement. Preoperative radiography, the standard for treatment decision making, provides limited information on the extent of degeneration, emphasizing the need for quantitative intraoperative techniques. This study evaluates the potential of vibroacoustic signals generated from instrument-tissue interactions to assess cartilage degeneration severity. A total of 136 ex vivo cartilage specimens of varying degeneration severity, histologically graded using the OARSI score, were collected from 41 patients undergoing arthroplasty. The specimens were classified into three groups: healthy cartilage (OARSI ≤2.0), degenerated cartilage (OARSI ≥ 2.5), and subchondral bone. Vibroacoustic signals were captured during specimen palpation using a vibration measurement system affixed to a surgical probe. 26 characteristic signal features were extracted using Continuous-Wavelet Transformation, and their discriminative power was assessed using Support Vector Machine and k-Nearest-Neighbor classifiers under patient- and specimen-level grouped cross-validation with nested hyperparameter tuning. Bone was distinguished from cartilage with 84-91% recall. Healthy cartilage (OARSI ≤2.0) was identifiable with 74-80% sensitivity, whereas the specificity for degenerated cartilage (OARSI ≥ 2.5) remained limited (40-57%), constituting the principal limitation of the approach. Interpreting the binary cartilage classification as a diagnostic test, a receiver-operating characteristic (ROC) analysis yielded an area under the curve (AUC) of up to 0.66 (95% CI [0.56, 0.76]). The study demonstrates that vibroacoustic signals from instrument-tissue interactions may provide complementary information for intraoperative cartilage evaluation and may contribute to decision making in arthroplasty, while the reliable grading of cartilage degeneration remains an open challenge.

Indexed as

Bone and BonesCartilage, ArticularOsteoarthritis, KneeFemaleHumansMaleSupport Vector MachineVibrationarticular cartilagecomputer-assisted surgerycontinuous wavelet transformdecision supportintraoperative tissue characterizationknee arthroplastyknee osteoarthritismachine learningsurgical data sciencevibroacoustic sensing

Identifiers

PMID42590649
PMCPMC13468883

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.