Automated segmentation of the healed anterior cruciate ligament from T<sub>2</sub>* relaxometry MRI scans

Flannery, Sean W.; Barnes, Dominique A.; Costa, Meggin Q.; Menghini, Danilo; Kiapour, A. M.; Walsh, Edward G.; Team, Bear Trial; Kramer, Dennis E.; Murray, Martha M.; Fleming, Braden C.

doi:10.1002/jor.25390

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Since 2021, there has been an exponential increase in studies on custom architecture CNNs for the diagnosis of ACL injuries applied to MRI, and currently there are various DL models developed, such as VGG16, VGG19, U-Net, AdaBoost, XGBoost, Xception, MRPyrNet, Inception ResNet-v2, RadImageNet, and Inception-v3 DTL [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Awan et al introduced a method that utilizes a tailored 14-layer ResNet-14 configuration of a CNN, which processes data in six distinct directions.…”

Section: Diagnosismentioning

confidence: 99%

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

Andriollo,

Picchi,

Sangaletti

et al. 2024

Healthcare

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The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

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Section: Diagnosismentioning

confidence: 99%

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

Andriollo,

Picchi,

Sangaletti

et al. 2024

Healthcare

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“…The ACL/graft and posterior cortex of the femoral diaphysis were manually segmented by an experienced examiner (AMK) with a high intra-rater reliability (ICC > 0.9 for segmenting intact and surgically treated ACLs/grafts) [37][38][39] in the CISS images using image processing software (Mimics v17.0; Materialize). In a few cases, the presence of surgical hardware resulted in artifacts around tunnels which did not affect the ACL segmentations.…”

Section: Mr Imagingmentioning

confidence: 99%

Comprehensive evaluation of magnetic resonance imaging sequences for signal intensity based assessment of anterior cruciate ligament healing following surgical treatment

Kaushal,

Kim,

Singh

et al. 2024

Journal Orthopaedic Research

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Normalized signal intensity (SI) obtained from magnetic resonance imaging (MRI) has been used to track anterior cruciate ligament (ACL) postoperative remodeling. We aimed to assess the effect of MRI sequence (PD: proton density‐weighted; T2: T2‐weighted; CISS: constructive interference in steady state) on postoperative changes in healing ACLs/grafts. We hypothesized that CISS is better at detecting longitudinal SI and texture changes of the healing ACL/graft compared to the common clinical sequences (PD and T2). MR images of patients who underwent ACL surgery were evaluated and separated into groups based on surgical procedure (Bridge‐Enhanced ACL Repair (BEAR; n = 50) versus ACL reconstruction (ACLR; n = 24)). CISS images showed decreasing SI across all timepoints in both the BEAR and ACLR groups (p < 0.01), PD and T2 images showed decreasing SI in the 6‐to‐12‐ and 12‐to‐24‐month postoperative timeframes in the BEAR group (p < 0.02), and PD images additionally showed decreasing SI between 6‐ and 24‐months postoperation in the ACLR group (p = 0.02). CISS images showed texture changes in both the BEAR and ACLR groups, showing increases in energy and decreases in entropy in the 6‐to‐12‐ and 6‐to‐24‐month postoperative timeframes in the BEAR group (p 0.04), and increases in energy, decreases in entropy, and increases in homogeneity between 6 and 24 months postoperation in the ACLR group (p < 0.04). PD images showed increases in energy and decreases in entropy between 6‐ and 24‐months postoperation in the ACLR group (p < 0.008). Finally, CISS was estimated to require a smaller sample size than PD and T2 to detect SI differences related to postoperative remodeling.

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Automated segmentation of the healed anterior cruciate ligament from T₂* relaxometry MRI scans

Abstract: Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T 2 * relaxometry. However, T 2 * mapping requires manual image segmentation, which is a time-consuming process and prone to inter-and intra-segmenter variability.

Cited by 2 publications

References 31 publications

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

Comprehensive evaluation of magnetic resonance imaging sequences for signal intensity based assessment of anterior cruciate ligament healing following surgical treatment

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Automated segmentation of the healed anterior cruciate ligament from T2* relaxometry MRI scans

Abstract: Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T 2 * relaxometry. However, T 2 * mapping requires manual image segmentation, which is a time-consuming process and prone to inter-and intra-segmenter variability.

Cited by 2 publications

References 31 publications

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

Comprehensive evaluation of magnetic resonance imaging sequences for signal intensity based assessment of anterior cruciate ligament healing following surgical treatment

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Automated segmentation of the healed anterior cruciate ligament from T₂* relaxometry MRI scans