The Forgotten Ligament: Why PCL Lags Behind ACL in AI Research
Of 29 deep learning studies on cruciate ligament injury detection, only one addresses the PCL. Why the posterior cruciate ligament remains AI's blind spot — and what needs to change.
The Numbers Tell the Story
A 2025 systematic review examined every published study using deep learning to detect cruciate ligament injuries from radiographic images. The result was striking: of 29 studies meeting inclusion criteria, 28 focused on the anterior cruciate ligament. Only one — a single study — addressed the posterior cruciate ligament.
This is not because the PCL is clinically unimportant. It is because the data infrastructure that enabled ACL AI research simply does not exist for the PCL.
Why the Disparity?
Three factors explain why AI research has largely ignored the PCL.
Incidence asymmetry drives data availability. ACL injuries account for approximately 52% of knee ligament trauma, while PCL injuries represent only 2.9%. Lower incidence means fewer cases in hospital databases, fewer labelled datasets, and fewer opportunities to train supervised learning models. Stanford's MRNet — the dataset that launched knee MRI AI research — contains labels for ACL tears, meniscal tears, and general abnormalities. It contains no PCL annotations. Without a benchmark dataset, researchers default to the problem they can measure.
Diagnostic confidence creates a false sense of sufficiency. MRI sensitivity for acute PCL tears is high — approaching 96–100% in expert hands. This creates an impression that PCL diagnosis is a solved problem. But the aggregate numbers mask a critical weakness: sensitivity for chronic PCL tears (6+ weeks post-injury) drops to 62.5%, and for post-reconstruction PCL graft tears, it falls to just 18.1%. The PCL's signal normalises within six months of injury, rendering chronic tears nearly invisible on conventional MRI sequences. This is precisely the scenario where AI assistance would be most valuable — and precisely where no models exist.
Research incentives favour high-volume problems. Academic publishing rewards novelty and impact. A deep learning model for ACL detection has a larger potential user base, more available training data, and a clearer path to clinical validation than an equivalent PCL model. The result is a self-reinforcing cycle: more ACL data leads to more ACL papers, which generates more ACL datasets, which attracts more ACL researchers.
Clinical Significance of the Gap
The PCL is the strongest ligament in the knee, providing approximately 95% of the total restraining force to posterior tibial translation. Despite its strength, PCL injuries are more common than historically recognised — accounting for up to 20% of acute knee injuries presenting to emergency departments and 2.4% of injuries in professional American football players.
The diagnostic challenge with PCL injuries is not the acute tear — it is everything after. Chronic PCL deficiency leads to a characteristic pattern of degenerative change in the medial compartment and patellofemoral joint, but the timeline and severity of this progression vary significantly between patients. Identifying which patients will develop symptomatic instability versus those whose compensatory function is restraining posterior tibial translation.
PCL injuries typically result from high-energy mechanisms — dashboard injuries in motor vehicle accidents, falls onto a flexed knee, or hyperextension injuries in contact sports. Isolated PCL injuries are relatively uncommon; most occur in the context of multi-ligament knee injuries, which complicates both diagnosis and treatment planning.
The treatment decision — conservative management versus surgical reconstruction — depends on injury grade, chronicity, associated injuries, and functional demands. This is exactly the type of multi-factorial clinical decision where AI-assisted analysis could add value, integrating imaging findings with clinical variables to support shared decision-making.
What Would PCL AI Require?
Building reliable AI models for PCL assessment would require addressing several challenges that do not apply — or apply less severely — to ACL research.
Dedicated annotation. Existing knee MRI datasets need PCL-specific labels: intact, partial tear, complete tear, chronic versus acute distinction, localisation (proximal, midsubstance, distal avulsion), and associated injuries (posterolateral corner involvement, meniscofemoral ligament status). This annotation work is labour-intensive and requires fellowship-trained musculoskeletal radiologists or experienced knee surgeons.
Multi-centre data. A single-centre dataset will not capture the MRI protocol, field strength, and patient population diversity needed for generalisable models. PCL tears are uncommon enough that any single institution may see only 20–50 cases per year with full MRI documentation. Federated learning approaches — where models train across institutions without centralising data — may be necessary.
Multi-sequence fusion. The PCL is best evaluated on sagittal and coronal sequences, with supplementary information from axial views. Models need to integrate information across sagittal, coronal, and axial planes — the same multi-sequence fusion approach described in our ACL assessment overview.
Temporal features for chronicity detection. Distinguishing acute from chronic PCL tears is one of MRI's known weaknesses. AI models that can detect subtle signal changes — residual scarring patterns, posterior tibial subluxation, secondary arthritic changes — may outperform conventional radiologist interpretation for this specific task.
Emerging Research: Ultrasound as an Alternative
A January 2026 study published in npj Artificial Intelligence introduced a deep learning framework for PCL assessment using ultrasound rather than MRI. The approach exploits the acoustic window over the popliteal fossa, where the PCL — despite being intra-articular — lies relatively close to the skin surface.
This is significant for two reasons. First, it represents one of the first dedicated AI tools for PCL evaluation. Second, ultrasound is cheaper, faster, and more widely available than MRI — enabling point-of-care PCL assessment in emergency departments, sports medicine clinics, and resource-limited settings.
The reported sensitivity and specificity of point-of-care ultrasound for PCL injuries ranges from 83–100% and 87–100%, respectively.
Salnus's Position
Our current AI development pipeline focuses on two modalities: knee radiograph analysis (KL grading, joint space width measurement, mechanical axis alignment) and knee MRI assessment (ACL detection as the initial use case). PCL assessment is on our research roadmap but requires the multi-centre annotated dataset that does not yet exist.
We are actively seeking clinical partners willing to contribute retrospectively annotated MRI data. If you are a knee surgeon or sports medicine specialist with access to PCL injury imaging data and interest in collaborative research, we would welcome the conversation.
The PCL may be the forgotten ligament in AI research, but it will not remain so indefinitely. The clinical need is real, the technical approaches exist, and the gap in the literature is an opportunity — not a barrier.
References:
- Mercurio M, et al. Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review. Diagnostics. 2025;15(6):776.
- Xue Y, et al. A novel deep learning based automatic ultrasonic posterior cruciate ligament clinical assessment tool. npj Artificial Intelligence. 2026.
- Bien N, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med. 2018;15(11):e1002699.
Disclaimer: This article is for educational and research purposes only. AI tools referenced are designated for research use only (RUO) and are not cleared medical devices. Clinical decisions regarding PCL management should be made by qualified physicians based on comprehensive clinical and imaging assessment.
Reviewed by the Salnus biomedical engineering team.