Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation

AIM

We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep learning (DL) model.

METHODS

We obtained preoperative MR images from 88 knees of patients who underwent arthroscopic meniscal repair, with or without ACL reconstruction. Ninety-eight MR images of knees without signs of meniscus or ACL tears were obtained from a publicly available database after matching on age and ACL status (normal or torn), resulting in a global dataset of 186 MRI examinations. The Keros® (Incepto, Paris) DL algorithm, previously trained for the detection and characterization of meniscus and ACL tears, was used for MRI assessment. Magnetic resonance images were individually, and blindly annotated by three physicians and the DL algorithm. After three weeks, the three human raters repeated image assessment with model assistance, performed in a different order.

RESULTS

The Keros® algorithm achieved an area under the curve (AUC) of 0.96 (95% CI 0.93, 0.99), 0.91 (95% CI 0.85, 0.96), and 0.99 (95% CI 0.98, 0.997) in the detection of medial meniscus, lateral meniscus and ACL tears, respectively. With model assistance, physicians achieved higher sensitivity (91% vs. 83%, p = 0.04) and similar specificity (91% vs. 87%, p = 0.09) in the detection of medial meniscus tears. Regarding lateral meniscus tears, sensitivity and specificity were similar with/without model assistance. Regarding ACL tears, physicians achieved higher specificity when assisted by the algorithm (70% vs. 51%, p = 0.01) but similar sensitivity with/without model assistance (93% vs. 96%, p = 0.13).

CONCLUSIONS

The current model consistently helped physicians in the detection of medial meniscus and ACL tears, notably when they were combined.

LEVEL OF EVIDENCE

Diagnostic study, Level III.

High prevalence of associated injuries in anterior cruciate ligament tears: A detailed magnetic resonance imaging analysis of 254 patients

OBJECTIVES

To evaluate the type and prevalence of associated injuries by using magnetic resonance imaging (MRI) in patients with anterior cruciate ligament (ACL) tears.

METHODS

Data from the Natural Corollaries and Recovery after ACL injury multicenter longitudinal cohort study were analyzed. Between May 2016 and October 2018, patients aged between 15 and 40 years, who had experienced an ACL tear within the last 6 weeks and sought medical attention at one of seven healthcare clinics in Sweden, were invited to participate. The mean time from injury to MRI was 19.6 ± 15.2 days. An orthopedic knee surgeon and a musculoskeletal radiologist reviewed all the MRI scans. The following structures were assessed: posterior cruciate ligament (PCL), medial collateral ligament (MCL) complex, lateral collateral ligament (LCL), popliteus tendon, medial meniscus (MM), lateral meniscus (LM), and cartilage. In addition, the presence of bone bruising, impaction fractures in the lateral femoral condyle (LFC) or posterolateral tibia (PLT), and Segond fractures were also assessed.  RESULTS: A total of 254 patients (48.4% males) with a mean age of 25.4 ± 7.1 years were included. The prevalence of associated injuries was as follows: PCL (0.4%), MCL {41.3% [superficial MCL and deep MCL (dMCL) 16.5%; isolated dMCL 24.8%]}, LCL (2.4%), MM (57.4%), LM (25.2%), cartilage (15.0%), bone bruising (92.9%), impaction fracture in the LFC (45.7%) and PLT (4.7%), and Segond fracture (7.5%).

CONCLUSIONS

The prevalence of associated injuries in patients with ACL tears was high. The findings reported in this study may serve as a reference tool for orthopedic surgeons and radiologists in the diagnosis of associated injuries using MRI in patients with ACL tears.

Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis

This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly discriminative novelty score, which leverages the aleatoric semantic uncertainty as this is modeled in the class scores outputted by the YOLOv5-nano object detection (OD) model. To account for tissue continuity, we propose using the global scores (probability vector) when the model is applied to the entire sagittal sequence. The second module of the proposed pipeline constitutes the MINIROCKET timeseries classification model for determining whether a knee has an ACL tear. To better evaluate the generalization capabilities of our approach, we also carry out cross-database testing involving two public databases (KneeMRI and MRNet) and a validation-only database from University General Hospital of Larissa, Greece. Our method consistently outperformed (p-value<0.05) the state-of-the-art (SOTA) approaches on the KneeMRI dataset and achieved better accuracy and sensitivity on the MRNet dataset. It also generalized remarkably good, especially when the model had been trained on KneeMRI. The presented framework generated at least 2.1 times less carbon emissions and consumed at least 2.6 times less energy, when compared with SOTA. The integration of aleatoric semantic uncertainty-based scores into a novelty detection framework, when combined with the use of lightweight OD and timeseries classification models, have the potential to revolutionize the MRI-based injury detection by setting a new precedent in diagnostic precision, speed and environmental sustainability. Our resource-efficient framework offers potential for widespread application.

Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective

OBJECTIVE

To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process.

METHODS

Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment.

RESULTS

The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses.

CONCLUSION

ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy.

CLINICAL RELEVANCE STATEMENT

3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time.

KEY POINTS

• AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.

A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

The continual improvement in the field of medical diagnosis has led to the monopoly of using deep learning (DL)-based magnetic resonance imaging (MRI) for the diagnosis of knee injury related to meniscal injury, ligament injury including the cruciate ligaments, collateral ligaments and medial patella-femoral ligament, and cartilage injury. The present systematic review was done by PubMed and Directory of Open Access Journals (DOAJ), wherein we finalised 24 studies conducted on the accuracy of DL MRI studies for knee injury identification. The studies showed an accuracy of 72.5% to 100% indicating that DL MRI holds an equivalent performance as humans in decision-making and management of knee injuries. This further opens up future exploration for improving MRI-based diagnosis keeping in mind the limitations of verification bias and data imbalance in ground truth subjectivity.

Imaging of Anatomical Variants Around the Knee

Several anatomical variants have been described in the knee. These variants may involve intra- and extra-articular structures, such as menisci, ligaments, plicae, bony structures, muscles, and tendons. They have a variable prevalence, are generally asymptomatic, and are usually discovered incidentally in knee magnetic resonance imaging examinations. A thorough knowledge of these findings is essential to avoid overestimating and overinvestigating normal findings. This article reviews most anatomical variants around the knee, describing how to avoid misinterpretation.

MR Imaging of the Lower Limb: Pitfalls, Tricks, and Tips

The purpose of this article is to discuss most common diagnostic pitfalls of the lower limb with specific attention to the knee, ankle, and foot joints. The knowledge of normal anatomic variants, correlation with age, symptoms, and medical history together with these potential MR imaging pitfalls is fundamental for an accurate interpretation of the imaging findings of the lower limb.

Particularities on Anatomy and Normal Postsurgical Appearances of the Knee

Detailed knowledge of anatomy helps to understand pathologic processes. This article focuses on the anatomy and functionality of the knee, with emphasis on recently studied concepts and anatomic features that have an association with the development of pathology. The most common anatomic variants posing a challenge for diagnosis and other common findings in asymptomatic patients are reviewed. Good understanding of the different surgical procedures helps in providing as much information as possible to guarantee a positive outcome, improving prognosis. We review what are the commonly expected postsurgical appearances and the most common postsurgical complications.

CT-like MRI: a qualitative assessment of ZTE sequences for knee osseous abnormalities

OBJECTIVE

To qualitatively evaluate the utility of zero echo-time (ZTE) MRI sequences in identifying osseous findings and to compare ZTE with optimized spoiled gradient echo (SPGR) sequences in detecting knee osseous abnormalities.

MATERIALS AND METHODS

ZTE and standard knee MRI sequences were acquired at 3T in 100 consecutive patients. Three radiologists rated confidence in evaluating osseous abnormalities and image quality on a 5-grade Likert scale in ZTE compared to standard sequences. In a subset of knees (n = 57) SPGR sequences were also obtained, and diagnostic confidence in identifying osseous structures was assessed, comparing ZTE and SPGR sequences. Statistical significance of using ZTE over SPGR was characterized with a paired t-test.

RESULTS

Image quality of the ZTE sequences was rated high by all reviewers with 278 out of 299 (100 studies, 3 radiologists) scores ≥ 4 on the Likert scale. Diagnostic confidence in using ZTE sequences was rated "very high confidence" in 97%, 85%, 71%, and 73% of the cases for osteophytosis, subchondral cysts, fractures, and soft tissue calcifications/ossifications, respectively. In 74% of cases with osseous findings, reviewer scores indicated confidence levels (score ≥ 3) that ZTE sequences improved diagnostic certainty over standard sequences. The diagnostic confidence in using ZTE over SPGR sequences for osseous structures as well as abnormalities was favorable and statistically significant (p < 0.01).

CONCLUSION

Incorporating ZTE sequences in the standard knee MRI protocol was technically feasible and improved diagnostic confidence for osseous findings in relation to standard MR sequences. In comparison to SPGR sequences, ZTE improved assessment of osseous abnormalities.

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5-100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

Meniscal ramp lesions: an illustrated review

The purpose of this review is to describe the anatomy and lesions affecting the peripheral portion of posterior horn of medial menisci (ramp lesions), along with illustrations and MRI cases. We will correlate imaging features with arthroscopic classification of ramp lesions. Also, postoperative and chronic changes related to meniscocapsular tears will be presented, as well as biomechanical consequences and treatment approach.

MRI of Traumatic Knee Dislocation: A Study to Evaluate Safety and Image Quality for Patients with Knee-Spanning Stabilization Devices

This study evaluated safety and image quality of MRI exams performed for patients with traumatic knee dislocations in knee-spanning stabilization devices. It is an IRB-approved retrospective design with waived informed consent that included 63 patients with traumatic knee dislocation. 56 patients had metallic external fixators, and 7 patients had non-metallic knee immobilizers. 7 patients had bilateral dislocations yielding a total of 70 knee MRIs. 1.5 Tesla MRI exams were performed for all patients who were awake and alert at the time of imaging. All knee-spanning external fixators were considered "MR conditional" by the FDA. The electronic medical record was reviewed for notes from the technologist and nursing staff documenting any patient complaints or adverse events during the MRI exam as required by departmental protocol. Qualitative analysis of the six most frequently performed sequences were independently conducted by 2 musculoskeletal radiologists using a 5-point Likert scale. Overall image quality and select time intervals between the two groups were compared using an independent sample t test and the Mann-Whitney U test, respectively. No adverse events were reported for a 40-minute average estimated patient scan time with the stabilization devices in the MR gantry. Mean values of Likert scale scores were generated from two readers' data for comparison between the external fixation and the immobilizer groups. Most knee MRI exams with external fixators were within diagnostic quality despite artifacts (grade 3). MRI exams generally were of higher diagnostic quality in the immobilizer group than the external fixator group (p < 0.05). The external fixator models included DePuy Synthes, Smith and Nephew, Stryker Hoffman III, Zimmer FastFrame, and Zimmer XtraFix. MRI examinations in patients with external fixators for traumatic knee dislocations can be safely performed under certain conditions and provide diagnostic quality images.

Magnetic resonance imaging of the knee

Knee pain is frequently seen in patients of all ages, with a wide range of possible aetiologies. Magnetic resonance imaging (MRI) of the knee is a common diagnostic examination performed for detecting and characterising acute and chronic internal derangement injuries of the knee and helps guide patient management. This article reviews the current clinical practice of MRI evaluation and interpretation of meniscal, ligamentous, cartilaginous, and synovial disorders within the knee that are commonly encountered.

The anterior knee: normal variants, common pathologies, and diagnostic pitfalls on MRI

The anterior aspect of the knee is host to an array of normal variants and potential pathology. These normal anatomic variants are often encountered and may mimic pathologies, leading to unnecessary work-up and treatments. On the other hand, there are several subtle abnormalities that may be easily overlooked or mistaken for variants or other injuries or diseases. Recognition of these diagnostic challenges is essential for radiologists to make an accurate diagnosis. This article reviews normal anatomical variants of ligaments, tendons, bones, and other important structures of the anterior knee, focusing on magnetic resonance imaging features. Commonly encountered injuries and abnormalities of the anterior knee and their diagnostic pitfalls are also discussed, highlighting findings on magnetic resonance imaging.

Clinics in diagnostic imaging (177). Medical meniscus bucket-handle tear with medial oblique meniscomeniscal ligament

A 29-year-old man with a previous football injury to his left knee presented with pain of the same knee. The patient twisted it as he was turning a corner quickly while going up the stairs, leading to internal rotation of his femur on his tibia with his knee in flexion. MR imaging revealed a bucket-handle tear of the medial meniscus, as well as a complete tear of the anterior cruciate ligament. However, image interpretation was complicated by the presence of a medial oblique meniscomeniscal ligament, a rare normal variant among intermeniscal ligaments of the knee. All four recognised variants of intermeniscal ligaments are discussed, with emphasis on their prevalence, imaging and anatomical features, and the way in which they may mimic meniscal tears.

Common Mistakes and Pitfalls in Magnetic Resonance Imaging of the Knee

This pictorial review presents an overview of common interpretation errors and pitfalls in magnetic resonance imaging (MRI) of the knee. Instead of being exhaustive, we will emphasize those pitfalls that are most commonly encountered by young residents or less experienced radiologists.