Artificial Intelligence in Repairing Meniscus Injury in Football Sports with Perovskite Nanobiomaterials

Wang, Wei

doi:10.1155/2021/4324138

Cited by 5 publications

(6 citation statements)

References 29 publications

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“…Before and after the evaluation, when the patient's muscles were completely relaxed and when there was no dynamic support of the Lokomat system, L-ROM was used to evaluate the range of motion of the affected knee joint by measuring the arc of motion through which the joint was moved by external force [22]. (2) In the observation of knee function, Lysholm scores and the International Knee Documentation Committee (IKDC) scores of knees were used to measure knee reoperation before and 6 months after labor. (3) To determine quality of life, the World Health Organization Quality of Life Scale-Short Form Questionnaire (WHO-QOL-BREF) was used to determine quality of life in two groups before and after surgery: WHOQOL-BREF included social work and life support and included physical and mental activity [23].…”

Section: Observation Indicatorsmentioning

confidence: 99%

“…With social progress, economic development, the improvement of residents' living standards, and the rise of national sports, people are paying more and more attention to their physical fitness and health, which makes more people join sports. In the process of exercising, due to the lack of understanding of the exercise load, exercise time, and correct exercise posture that oneself can bear, it is bound to cause sports injuries in some parts of the body [ 1 , 2 ]. The meniscus is the cartilage between the tibia and the femur, one on each side.…”

Section: Introductionmentioning

confidence: 99%

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Management Strategies and Imaging Observation of Early and Delayed Intelligent Treatment of Meniscus Sports Injury under Knee Osteoarthroscopy

Deng

Guo

et al. 2022

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Objective. To investigate the meniscus characteristics of knee osteoarthritis and its guiding significance for minimally invasive surgery. Methods. A total of 100 patients with knee meniscus sports injuries who were treated in our hospital from January 2019 to January 2022 were selected as the research subjects and were grouped according to the interval between injury and surgery, with an interval of 2 months: the early group (53 cases) within 2 months and the delayed group (47 cases) with an interval of more than 2 months. The distribution of intraoperative complications in the two groups was observed and recorded, and the changes in pain degree, joint range of motion, knee joint function, and quality of life scores before and after operation were compared between the two groups. Results. The postoperative VAS score, range of motion, Lysholm score, IKDC knee subjective function score, and quality of life score were significantly improved in both groups ( P < 0.05 ). The incidence of intra-articular cartilage injury in the delayed group was significantly higher than that in the early group ( P < 0.05 ). The patellofemoral cartilage injury was the main part of intra-articular cartilage injury in the two groups, and the incidence of patellofemoral cartilage injury in the delayed group was significantly higher than that in the early group ( P < 0.05 ). The cartilage damage was mainly cartilage damage, and the grades I-II and III-IV cartilage damages were significantly increased in the extension group. Conclusion. Meniscal injury in knee osteoarthritis has certain microscopic characteristics. In this paper, the microscopic classification of meniscus injury in knee osteoarthritis is helpful to guide microscopic surgery and improve the minimally invasive knee osteoarthritis effect of surgical treatment.

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Section: Observation Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Management Strategies and Imaging Observation of Early and Delayed Intelligent Treatment of Meniscus Sports Injury under Knee Osteoarthroscopy

Deng

Guo

et al. 2022

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“…Recent research has identified artificial intelligence (AI) as a tool to predict the need for overnight hospital stays in ligament surgery and total knee arthroplasty (TKA) [ 8 ]. AI has also contributed to new advanced treatments of meniscus injuries [ 9 ]. Orthopedic surgery has made significant progress by adapting new techniques as arthroscopy in the last century and machine learning possess another potential for a ground breaking shift towards optimal treatment.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

Kulseng¹,

Nainamalai

Grøvik

et al. 2023

BMC Musculoskelet Disord

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Background To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness. Methods The sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The Dice similarity coefficient (DSC), Jaccard index and Hausdorff were used to compare the performance of the networks. Results Combining all sequences collectively performed significantly better than other alternatives. The following DSCs (±standard deviation) were obtained for the test dataset: Bone medulla 0.997 (±0.002), PCL 0.973 (±0.015), ACL 0.964 (±0.022), muscle 0.998 (±0.001), cartilage 0.966 (±0.018), bone cortex 0.980 (±0.010), arteries 0.943 (±0.038), collateral ligaments 0.919 (± 0.069), tendons 0.982 (±0.005), meniscus 0.955 (±0.032), adipose tissue 0.998 (±0.001), veins 0.980 (±0.010) and nerves 0.921 (±0.071). The deep learning network correctly identified the anterior cruciate ligament (ACL) tear of the left knee, thus indicating a future aid to orthopaedics. Conclusions The convolutional neural network proves highly capable of correctly labeling all anatomical structures of the knee joint when applied to 3D MR sequences. We have demonstrated that this deep learning model is capable of automatized segmentation that may give 3D models and discover pathology. Both useful for a preoperative evaluation.

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“…4IR has attracted considerable academic attention as a new trend towards automation and as a positive response to the lack of growth of new industries [17]. The field of sports medicine under the influence of 4IR has also seen a part of emerging research in the 21st century, such as sports medicine and artificial intelligence [8], biomimetic materials in sports medicine [18,19], the use of big data in sports medicine [20,21]. The impact of 4IR on the field of sports medicine is also much more than what has been mentioned, according to a search on PubMed it was found that keywords related to 4IR…”

Section: Technologies Directly Related To 4irmentioning

confidence: 99%

A Comparison Study of Sport Medicine Research Trend Before and After the 4th Industrial Revolution: A Systematic Review

2023

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OBJECTIVES The purpose of the study is to explore the changing trend of research keywords in the field of sports medicine the contributions of outstanding authors, institutions, and countries participating in the research, and the technologies directly related to the 4th Industrial Revolution (4IR) in the field of 4IR sports medicine before and after 4IR.METHODS The sample data of this study was retrieved from all the bibliographic information of the top two journals in the sports medicine field (i.e. Sports Medicine and British Journal of Sports Medicine). A bibliometric analysis was carried out by using CiteSpace, and python was used for further text mining on the key nodes in the analysis results.RESULTS The study has found that cardiovascular disease has always been popular in the research field of sports medicine; improving people’s quality of life and avoiding the risk of disease through physical activity, as long as research related to cruciate ligament may appear frequently again in the next few years. After 4IR, the field of sports medicine began to shift from a single study of epidemiology to multidimensional research of epidemiology, preventive medicine and psychology; the population characteristics of the researches began to be more segmented. Also, more collaboration between authors, institutions and institutions involved in sports medicine research has found. Germany and hospitals have become emerging forces after 4IR, and meta-analysis and systematic review are common concerns of Germany and hospitals. The impact of 4IR on sports medicine is mainly manifested in the application of sensors, intelligent monitoring, metabolomics and genomics, which having a great prospect in application.CONCLUSIONS The study sorted out the changes in research trends in the field of sports medicine before and after 4IR, and at the same time proved that the impact of 4IR on sports medicine has already existed, and more scholars need to pay attention. 4IR has brought new forces and new trends to the field of sports medicine, which deepened the research cooperation in this field. Wearable devices, intelligent monitoring, metabolomics, and genomics have applied and had a positive developing trend in sports medicine field.

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Artificial Intelligence in Repairing Meniscus Injury in Football Sports with Perovskite Nanobiomaterials

Cited by 5 publications

References 29 publications

Management Strategies and Imaging Observation of Early and Delayed Intelligent Treatment of Meniscus Sports Injury under Knee Osteoarthroscopy

Management Strategies and Imaging Observation of Early and Delayed Intelligent Treatment of Meniscus Sports Injury under Knee Osteoarthroscopy

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

A Comparison Study of Sport Medicine Research Trend Before and After the 4th Industrial Revolution: A Systematic Review

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