Artificial Intelligence-Based Identification of the Target Zone on Arthroscopic Images During Knee Anterior Cruciate Ligament Reconstruction: An Observational Study

Background and rationale. Anterior cruciate ligament (ACL) reconstruction is one of the most frequently performed orthopaedic procedures worldwide. A well-documented determinant of long-term clinical outcome is the anatomical accuracy of femoral bone tunnel placement, whose centre must reproduce the native ACL footprint on the lateral wall of the intercondylar notch. Femoral tunnel malposition is recognised in the literature as a contributing factor to recurrent instability, graft failure and revision surgery. Anatomic tunnel placement is guided arthroscopically by the visual identification of bony and cartilaginous landmarks - most notably the resident's ridge (lateral intercondylar ridge) and the posterior cartilaginous margin of the lateral femoral condyle. These landmarks must be discriminated from the surrounding tissue on the 2D arthroscopic monitor image; their identification depends on operator experience and on the quality of the visual field, which can be degraded by soft-tissue debris, bleeding or suboptimal camera orientation. The reported association between surgeon case volume and surgical outcome reflects, in part, the learning curve associated with this visual task. Computer vision and artificial intelligence methods applied to surgical video offer a route to automated, reproducible recognition of these landmarks on the existing arthroscopic image stream, without modifying the hardware already present in the operating room.

Investigational system. The ARS (Augmented Reality in Surgery) software pipeline analyses arthroscopic video acquired from the standard arthroscopic camera column. Two functional modules are evaluated: a binary classifier that determines whether the field of view is sufficiently free of debris and adequately oriented to allow landmark identification, and a semantic segmentation module that delineates the resident's ridge, the posterior cartilaginous margin of the lateral femoral condyle and the candidate anatomical footprint zone for femoral tunnel placement. The algorithmic core is built by fine-tuning surgical vision foundation models on a dataset specifically curated for this task. The intended future deployment paradigm - outside the scope of the present study - is an unobtrusive visual overlay on the existing arthroscopic monitor, supporting the surgeon's visual interpretation. By design, the system is a decision-support tool: it does not pilot instruments, does not take autonomous decisions, and does not substitute the surgeon's judgement at any step of the procedure.

Study design. The study is a prospective, single-centre observational study conducted at IRCCS Galeazzi-Sant'Ambrogio (Milan, Italy). A retrospective component, based on previously acquired arthroscopic video material from ACL reconstructions performed within the same Unit and managed under applicable data-protection and consent provisions, is used for additional independent validation of the algorithms. The prospective component is fully integrated into the standard clinical pathway: no additional surgical step, device or instrument is introduced, no operative time is added, and the system is not used to guide any intraoperative decision during enrolment. The algorithm operates offline on the recorded material.

Video acquisition protocol. For each enrolled patient, six 5-second video segments are extracted from a single continuous intra-operative recording captured from the existing arthroscopic camera column at native 1920×1080 resolution and 60 frames per second. Five segments document the state of the intercondylar notch at progressive cleaning steps - corresponding to approximately 0%, 25%, 50%, 75% and 100% completion of soft-tissue debridement of the lateral wall - and the sixth segment is acquired with the surgical probe positioned on the posterior cartilaginous margin, without occluding the candidate footprint zone, in order to capture the instrument-anatomy spatial relationship in the same anatomical frame. During the procedure the surgeon verbally announces each stability moment to facilitate post-operative segment extraction. Cleaning of the lateral wall in the resident's ridge region is performed exclusively with radiofrequency ablation; motorised instrumentation is avoided in this region during enrolment to preserve the integrity of the bony ridge as a visual landmark.

Data management. Footage is pseudonymised at the point of acquisition; only intra-articular content is recorded, and no patient-identifying frames are produced. A structured naming convention is applied at ingestion. All study data are stored on institutional infrastructure under the governance of IRCCS Galeazzi-Sant'Ambrogio, processed in accordance with Regulation (EU) 2016/679 (GDPR) and applicable Italian implementing legislation. Each video segment is traceable to a single enrolment record on the institutional study management system, supporting source-data verification.