Augmented Reality in Surgery

NCT07636278 | Recruiting

• 1 other identifier: ARS
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to learn whether an artificial-intelligence software can reliably recognise the anatomical landmarks used to guide femoral bone tunnel placement on the arthroscopic monitor image during anterior cruciate ligament (ACL) reconstruction in adults. The main questions it aims to answer are: Can the software automatically tell when the arthroscopic image is clean enough to allow identification of these landmarks? Can the software accurately outline the key bony and cartilaginous landmarks on the femur that guide correct tunnel positioning? Participants will undergo their clinically indicated ACL reconstruction without modifications: short video sequences of the operative field will be recorded from the arthroscopic camera already used in routine practice, and used to train and validate the algorithms. No additional devices, manoeuvres or operative time are required.

Conditions

Anterior Cruciate Ligament (ACL) Tear

Keywords

Anterior Cruciate Ligament Reconstruction; Arthroscopy; Artificial Intelligence; Deep Learning; Computer Vision; Semantic Segmentation; Augmented Reality; Surgery, Computer-Assisted; Image Processing; Femoral Tunnel; Decision Support Systems

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the binary classification of arthroscopic field cleanliness

  Area under the receiver operating characteristic curve (AUC-ROC) of the deep learning model trained to perform binary classification of arthroscopic frames into "fully cleaned" (corresponding to 100% completion of soft-tissue debridement of the lateral wall of the intercondylar notch) versus "not fully cleaned" (corresponding to 0%, 25%, 50% and 75% completion). AUC-ROC is computed on the independent validation cohort (frames extracted from the second 50 subjects, not used during training), with 95% confidence interval estimated by bootstrap. Additional descriptive performance metrics - overall classification accuracy, sensitivity, specificity, positive predictive value and F1-score - are pre-specified in the study protocol and reported as supportive.

  Through study completion, an average of 12 months

Secondary Outcomes (1)

• Mean Dice similarity coefficient of the semantic segmentation of anatomical landmarks on arthroscopic frames

  Through study completion, an average of 12 months

Study Arms (1)

Adult patients undergoing primary arthroscopic ACL reconstruction

Adult patients (age 18 years or older) consecutively enrolled at IRCCS Galeazzi-Sant'Ambrogio (Milan, Italy) for primary arthroscopic anterior cruciate ligament reconstruction. The procedure follows the institutional standard of care; no investigational device, additional intraoperative manoeuvre or operative-time extension is introduced for study purposes. During surgery, a continuous arthroscopic video is recorded from the standard arthroscopic camera column already in clinical use, and short segments are extracted to document the lateral wall of the intercondylar notch at progressive cleaning steps and the instrument-anatomy relationship at the fully-cleaned step. The recorded material is pseudonymised and used to train and validate computer vision algorithms for landmark identification on the arthroscopic image. The first 50 enrolled subjects contribute to algorithm training; the subsequent 50 to independent validation.

Procedure: Primary arthroscopic anterior cruciate ligament reconstruction

Interventions

Primary arthroscopic anterior cruciate ligament reconstruction PROCEDURE

Arthroscopic reconstruction of the anterior cruciate ligament performed per institutional surgical protocol. Cleaning of the lateral wall of the intercondylar notch in the resident's ridge region uses exclusively radiofrequency ablation; motorised instrumentation is avoided in this region to preserve the integrity of the bony landmark. Per enrolled patient, a continuous intra-operative recording is obtained from the unmodified standard arthroscopic camera column at 1920x1080 resolution and 60 fps; six 5-second segments are extracted, five documenting progressive cleaning steps of the lateral wall (0%, 25%, 50%, 75%, 100% completion) and one acquired with the surgical probe positioned on the posterior cartilaginous margin without occluding the candidate femoral footprint zone. The investigational software is not used to guide any intraoperative decision during enrolment; the algorithm operates offline on the recorded material.

Adult patients undergoing primary arthroscopic ACL reconstruction

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients referred to and treated at the Knee Surgery and Sports Traumatology Unit of IRCCS Galeazzi-Sant'Ambrogio in Milan, Italy. The institution is a tertiary orthopaedic referral hospital and a recognised Italian Scientific Hospital for Research and Care (Istituto di Ricovero e Cura a Carattere Scientifico), dedicated to the musculoskeletal system and performing a very high yearly volume of orthopaedic procedures. Within the institution, the Knee Surgery and Sports Traumatology Unit is a homogeneous surgical team with a substantial annual caseload of arthroscopic anterior cruciate ligament reconstructions. Enrolment is single-centre, prospective and consecutive across the study period.

You may qualify if:

• Age 18 years or older
• Scheduled for primary arthroscopic anterior cruciate ligament reconstruction at IRCCS Galeazzi-Sant'Ambrogio
• Signed written informed consent

You may not qualify if:

• Revision anterior cruciate ligament reconstruction
• Arthroscopic video quality judged inadequate by the investigator (artefacts, insufficient illumination, uninterpretable images)
• Failure to sign informed consent, or withdrawal of consent

Sponsors & Collaborators

• I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio: lead

Study Sites (1)

IRCCS Ospedale Galeazzi-Sant'Ambrogio

Milan, Michigan, 20157, Italy

RECRUITING

Related Publications (4)

• Kayaalp ME, Konstantinou E, Karaismailoglu B, Lucidi GA, Kaymakoglu M, Vieider R, Giusto JD, Inoue J, Hirschmann MT. The metaverse in orthopaedics: Virtual, augmented and mixed reality for advancing surgical training, arthroscopy, arthroplasty and rehabilitation. Knee Surg Sports Traumatol Arthrosc. 2025 Aug;33(8):3039-3050. doi: 10.1002/ksa.12723. Epub 2025 Jul 7.

  PMID: 40622017 BACKGROUND

• Bian D, Lin Z, Lu H, Zhong Q, Wang K, Tang X, Zang J. The application of extended reality technology-assisted intraoperative navigation in orthopedic surgery. Front Surg. 2024 Feb 5;11:1336703. doi: 10.3389/fsurg.2024.1336703. eCollection 2024.

  PMID: 38375409 BACKGROUND

• Chen H. Application progress of artificial intelligence and augmented reality in orthopaedic arthroscopy surgery. J Orthop Surg Res. 2023 Oct 14;18(1):775. doi: 10.1186/s13018-023-04280-9.

  PMID: 37838695 BACKGROUND

• Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Ann Surg. 2018 Jul;268(1):70-76. doi: 10.1097/SLA.0000000000002693.

  PMID: 29389679 BACKGROUND

MeSH Terms

Conditions

Lacerations

Condition Hierarchy (Ancestors)

Wounds and Injuries

Central Study Contacts

Andrea Cusumano, MD

CONTACT

0039 3483733690; dr.andreacusumano@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 23, 2026
• First Posted: June 9, 2026
• Study Start: May 26, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): May 31, 2027
• Last Updated: June 9, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)