Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume

Li, Mifang; Bai, Hanhua; Zhang, Feiyuan; Zhou, Yujia; Lin, Qiao; Zhou, Quan; Feng, Qianjin; Zhang, Lingyan

doi:10.1186/s12891-022-05378-7

Cited by 3 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the reconstruction of the anterior cruciate ligament, the so-called notchplasty to increase the intracondylar fossa, and in order to avoid frequent performance of this procedure, the preoperative application of MRI is introduced to display the structures of the knee joint using automatic segmentation based on DL due to its excellent ability to independently learn from the provided data (Figure 6). This Res-UNet model achieved a segmentation speed of 3 to 5 seconds, which is very useful compared to manual segmentation that required 10 minutes to achieve the same results, and this model proved to be useful in clinical application [46].…”

Section: Application Of Artificial Intelligence In Knee Injury Diagno...mentioning

confidence: 81%

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

Kujundžić,

Matijaš

2023

Radiol. vjesn. (Online)

View full text Add to dashboard Cite

Introduction: Technological progress leads to an increasing use of radiological imaging, and an increase in the number of imaging results in an increased workload for radiologists. The driver of the application of AI in radiology is considered to be the reduction of the workload of radiologists and the need for faster and more accurate diagnosis. Aim: The aim of this paper is to bring the reader closer to the implementation of AI in radiology, especially in the MRI modality, and how deep learning algorithms improve image reconstruction. Discussion: Numerous studies have confirmed the importance of implementing machine learning, a subset of artificial intelligence, in the radiology system. In this review paper, numerous researches on the application of deep learning in magnetic resonance imaging are highlighted, and the emphasis is on models for automatic segmentation. Automatic segmentation has shown excellent results in the early detection of osteoarthritis, then in anterior cruciate ligament and meniscus tears, the most common knee injuries, and more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation has achieved, above all, high accuracy and precision, objectivity and time saving. Conclusion: Previous research has already highlighted the significant advantage of using machine learning in radiology and the exceptional compatibility between the work of radiologists and machine learning, which achieves precise and quick diagnoses. All this is a great incentive for further research, and technological progress will certainly speed up its integration into clinical practice.

show abstract

Section: Application Of Artificial Intelligence In Knee Injury Diagno...mentioning

confidence: 81%

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

Kujundžić,

Matijaš

2023

Radiol. vjesn. (Online)

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Comparative Analysis of Convolutional Neural Network Architectures for Automated Knee Segmentation in Medical Imaging: A Performance Evaluation

Ghidotti,

Vitali,

Regazzoni

et al. 2024

Journal of Computing and Information Science in Engineering

View full text Add to dashboard Cite

Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, that is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumor, to segment knee bones from Magnetic Resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as Dice Similarity Coefficient and Hausdorff Distance. In the training phase, various combinations of hyper-parameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice Similarity Coefficients of 0.94 and Hausdorff Distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios.

show abstract

Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume

Cited by 3 publications

References 37 publications

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

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

Comparative Analysis of Convolutional Neural Network Architectures for Automated Knee Segmentation in Medical Imaging: A Performance Evaluation

Contact Info

Product

Resources

About