Predictive modeling for injury prevention in athletes using artificial intelligence

Dynamic balance is an important component in the performance of squash athletes because the sport demands high levels of directional change, posture control, and stability. This study aims to analyze the effect of the 8-week core stability training program on the dynamic balance of BKMF FIKK UNM squash players. This study uses a quantitative design with a one group pretest–posttest design approach. The research sample amounted to 19 athletes who were determined using the total sampling technique. Dynamic balance measurements are performed using the Y Balance Test. The results showed that the average score of the Y Balance Test increased from 90.68 ± 1.04 in the pretest to 95.79 ± 1.16 in the posttest with an average difference of 5.11. The normality test showed normal distribution data with a pretest significance value of 0.08 and a posttest value of 0.20. The results of the paired sample t-test showed a t-value of 63.95 with a significance of 0.00 (p < 0.05) and an effect size value of 4.67 which was included in the category of very large effects. These findings suggest that core stability training programs are effective in improving the dynamic balance of squash athletes. The results of this study make a practical contribution to the development of a core stability-based physical training program to improve movement performance and support injury prevention in athletes. Further research is recommended using experimental designs with control groups as well as adding other exercise performance variables to reinforce the generalization of the findings.

Balance ability has been associated with performance and injury prevention in athletes. Previous studies have investigated the differences in lower body dynamic balance abilities among male high school, collegiate and professional soccer players. To investigate the differences on the Upper Quarter Y-Balance Test (UQYBT) in school, collegiate and employed cricket players. Dynamic balance data were collected for school (SCH; n = 22), collegiate (COL; n = 19) and employed (EMP; n = 15) cricket players of Jamia Millia Islamia, New Delhi, India using the UQYBT standardized protocol. For the UQYBT, the participant reached with one hand in the medial, superolateral and inferolateral directions while maintaining three points of contact (other hand and both feet). The test was performed for both non-dominant and dominant hands. The SCH group had the least medial reach distance than the other two groups (p = 0.010). Also, SCH players tended to exhibit a lower composite reach score than the other groups, but this difference was not significant (p > 0.05). No bilateral differences were observed among the three groups in any of the reach directions. Upper body dynamic balance performance varied with the competition level. This may indicate that athletes' movement strategies may be different depending on the competition level and that normative values may need to be established for each competition level.

High prevalence and large economic impact of knee injuries related to athletic activities led to the development of different protocols for injury prevention, management and/or rehabilitation in the past decades, with the use of various knee braces among the most popular. Braces have been used to reduce knee injuries in top-level sport since the beginning (Wirth and DeLee, 1990), with their popularity exploding from the early 1980s, when a multitude of manufacturers started to promote several brace models for use in American football and other contact and non-contact sports. Yet, debate over their true efficacy was ongoing in medical circles because injuries did continue to occur despite the use of braces (Najibi and Albright, 2005). Today, with the advance of biomechanical techniques, functional approach and epidemiological investigations into the effectiveness of bracing, the use of functional knee braces in athletes with joint instability is seriously questioned. This chapter provides a review of the applicability of functional knee braces in reinjury prevention and for athletic performance. Besides clinical significance of knee injuries, the design of functional braces will be discussed, as well as the effects of knee bracing for injury prevention in athletes with insufficient knee ligaments, and its effectiveness on physiological parameters of sport performance in anterior cruciate-deficient knees.

With the rapid development of sports injury prevention and medical imaging technology, anterior cruciate ligament (ACL) injuries have gained significant importance in clinical diagnosis and athletic protection. Traditional diagnostic methods rely on clinicians interpreting magnetic resonance imaging (MRI) scans, a process that is often influenced by subjective experience and may lead to misdiagnosis or delayed treatment. To address these challenges and improve diagnostic accuracy and efficiency, this study proposes an automatic detection model that combines a 3D convolutional neural network (CNN) with a residual neural network. The conducted model incorporates a custom classification head along with Focal Loss and weighted random sampling strategies to mitigate data imbalance, while dynamic learning rate adjustment ensures stable convergence during training. After 1000 runs, the model achieved an accuracy of 91.65% and an AUC of 0.981 on the validation set, which represents an improvement of approximately 2 to 10% over existing methods. These promising results demonstrate the potential application of the proposed method in clinical decision support and athletic injury prevention.

Background Incidence rate of thyroid cancer is steadily increasing due to overdiagnosis and overtreatment. Thyroid ultrasound is commonly used to diagnose thyroid cancer. The aim of this study is to examine the accuracy of using deep convolutional neural network (DCNN) models to improve diagnosis of thyroid cancer by analyzing sonographic imaging data from clinical thyroid ultrasound. Methods A total of 131,731 sonographic images from 17,627 thyroid cancer patients and 180,668 sonographic images from 25,325 controls used as training set were obtained from Tianjin Cancer Hospital. Images from anatomical sites that did not have cancer according to location sign on the image were not included. All thyroid cancer patients and 13·2% of controls (51,255 images) were confirmed by pathological reports. DCNN is a specific type of neural network optimized for image recognition. We trained two DCNN models on the training set and subsequently evaluated the performance on one independent internal (Tianjin, 1,118 individuals) and two external (Jilin,154 individuals; Weihai, 1,420 individuals) validation sets. Individuals in the validation sets all have pathological examinations. We compared the specificity/sensitivity of DCNN models with the performance of six thyroid ultrasound radiologists on these three validation sets. Findings DCNN model achieved high performance in identifying thyroid cancer patients versus six experience radiologists: for Tianjin validation set, sensitivity was 92·2% versus 96·9% (95% CI 89·7% - 94·3% vs. 93·9% - 98·6%; p = 0·003), and specificity was 85·6% versus 59·4% (95% CI 82·4% - 88·4% vs. 53% - 65·6%; p < 0·0001); for Jilin validation set, sensitivity was 84·3% versus 92·9% (95% CI 73·6% - 91·9% vs. 84·1% - 97·6%; p = 0·05), and specificity was 86·9% versus 57·1% (95% CI 77·8% - 93·3% vs. 45·9% - 67·9%; p < 0·0001); for Weihai validation set, sensitivity was 84·5% versus 89% (95% CI 81·2% - 87·4% vs. 81·9% - 94%; p = 0·2), and specificity was 87·5% versus 68·6% (95% CI 85·1% - 89·6% vs. 60·7% - 75·8%; p < 0·0001). Interpretation DCNN models exhibited high accuracy, sensitivity, and specificity in identifying thyroid cancer patients at levels comparable to or higher than six experienced radiologists. Conferred by the high specificity of DCNN models, the rate of overdiagnosis and overtreatment of patients with thyroid cancer is expected to decrease. This supports future application of the deep learning models to clinical practice for thyroid cancer diagnosis. However, further validation of these DCNN models in prospective clinical trials is warranted. Funding The Program for Changjiang Scholars and Innovative Research Team in University in China (IRT_14R40), National Natural Science Foundation of China (31801117). Citation Format: Xiangchun Li, Sheng Zhang, Qiang Zhang, Xi Wei, Yi Pan, Jing Zhao, Xiaojie Xin, Xiaoqing Wang, Fan Yang, Jianxin Li, Meng Yang, Qinghua Wang, Xiangqian Zheng, Yanhui Zhao, Lun Zhang, Xudong Wang, Zhimin Zheng, Christopher T. Whitlow, Metin N. Gurcan, Boris Pasche, Ming Gao, Wei Zhang, Kexin Chen. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images from clinical ultrasound exams [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1394.

Recent experience in applying recurrent neural networks (RNNs) to interpreting Permannt Downhole Gauge (PDG) records has highlighted the potential utility of machine learning algorithms to learn reservoir behavior from data. The power of the RNN resides in its ability to retain information in a form of memory of previous patterns and information contained in the previous behavior of phenomena being modeled. This memory plays a role of informing the decision at the present time by using what happened in the past. This property suggests RNNs as a suitable choice to model sequences of reservoir information, even when the reservoir modeler is faced with incomplete knowledge of the underlying physical system. Convolutional Neural Networks (CNNs) are another variant of machine learning algorithms that have shown promise in sequence modeling domains, such as audio synthesis and machine translation. In this study, RNN and CNN were applied to tasks that traditionally would be modeled by a reservoir simulator. This was achieved by formulating the relationship between physical quantities of interest from subsurface reservoirs as a sequence mapping problem. In addition, the performance of a CNN layer as compared to RNN was evaluated systematically to investigate their capabilities in a variety of tasks of interest to the reservoir engineer. Preliminary results suggest that CNNs, with specific design modifications, are as capable as RNNs in modeling sequences of information and as reliable when making inferences to cases that has not been seen by the algorithm during training. Design details and reasons pertaining to the way these two seemingly different architectures process information, and handle memory are also discussed.

Seed freezing damage is an agricultural disaster, which has a great impact on seed quality assurance. The feasibility of combining hyperspectral imaging with deep convolutional neural network (DCNN) to classify different freeze-damaged corn seeds was studied in this paper. At first, the hyperspectral images of corn seeds subjected to five different freezing temperatures at 400–1000 nm were acquired, and then the average spectra were extracted from the region of embryo hyperspectral images over the wavelength range of 450–979 nm. Next, four models (K nearest neighbors (KNN), support vector machine (SVM), extreme learning machine (ELM), and DCNN) were developed for five-category (5 frozen conditions) and four-category (“no freezing,” “slight freezing,” “moderate freezing,” and “severe freezing”) classifications, and the values of the evaluation indexes (accuracy, sensitivity, specificity, and precision) were calculated for comparison. The results show that DCNN model had the most satisfactory result with accuracy rates of 100% (training set), 96.9% (validation set), and 97.5% (testing set) for five-category classification, with accuracy rates of 100% (training, validation, and testing set) for four-category classification, and DCNN model also had the best performance in the evaluation indexes. At last, the visual classification map was generated according to the results of DCNN. It shows that hyperspectral imaging and DCNN can provide a novel method to detect the freezing damage of corn seeds quickly and inexpensively.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

What's New in Spine Surgery.

Historically, the rotorcraft community has experienced a higher fatal accident rate than other aviation segments, including commercial and general aviation. To date, traditional methods applied to reduce incident rates have not proven hugely successful for the rotorcraft community. Recent advancements in artificial intelligence (AI) and the application of these technologies in different areas of our lives are both intriguing and encouraging. When developed appropriately for the aviation domain, AI techniques may provide an opportunity to help design systems that can address rotorcraft safety challenges. Our recent work demonstrated that AI algorithms could use video data from onboard cameras and correctly identify different flight parameters from cockpit gauges, e.g., indicated airspeed. These AI-based techniques provide a potentially cost-effective solution, especially for small helicopter operators, to record the flight state information and perform post-flight analyses. We also showed that carefully designed and trained AI systems can accurately predict rotorcraft attitude (i.e., pitch and yaw) from outside scenes (images or video data). Ordinary offthe-shelf video cameras were installed inside the rotorcraft cockpit to record the outside scene, including the horizon. The AI algorithm was able to correctly identify rotorcraft attitude at an accuracy in the range of 80%. In this work, we combined five different onboard camera viewpoints to improve attitude prediction accuracy to 94%. Our current approach, which is referred to as ensembled prediction, significantly increased the reliability in the predicted attitude (i.e., pitch and yaw). For example, in some camera views, the horizon may be obstructed or not visible. The proposed ensemble method can combine visual details recorded from other cameras and predict the attitude with high reliability. In our setup, the five onboard camera views included pilot windshield, co-pilot windshield, pilot Electronic Flight Instrument System (EFIS) display, co-pilot EFIS display, and the attitude indicator gauge. Using video data from each camera view, we trained a variety of convolutional neural networks (CNNs), which achieved prediction accuracy in the range of 79% to 90%. We subsequently ensembled the learned knowledge from all CNNs and achieved an ensembled accuracy of 93.3%. Our efforts could potentially provide a cost-effective means to supplement traditional Flight Data Recorders (FDR), a technology that to date has been challenging to incorporate into the fleets of most rotorcraft operators due to cost and resource constraints. Such cost-effective solutions can gradually increase the rotorcraft community's participation in various safety programs, enhancing safety and opening up helicopter flight data monitoring (HFDM) to historically underrepresented segments of the vertical flight community.

A hybrid model based on neural networks for biomedical relation extraction

The advent of artificial intelligence (AI) has revolutionized numerous fields, and the culinary domain is no exception. AI-driven recipe discovery, particularly through the identification of ingredients from food images and the generation of recipe instructions, has the potential to transform the way recipes are created and personalized. This survey explores the state-of-the-art techniques in ingredient identification and recipe generation, focusing on leveraging deep learning models and computer vision for extracting relevant data from food images. We review the various methods employed for ingredient recognition, such as Convolutional Neural Networks (CNNs) and object detection models, alongside the use of sequence modeling techniques like Recurrent Neural Networks (RNNs) and Transformer models for recipe generation. The survey highlights key challenges, such as ingredient ambiguity, contextual understanding in cooking processes, and dataset limitations. Furthermore, it provides an in-depth evaluation of current AI models based on accuracy, creativity, and practicality in recipe generation. Finally, we propose future research directions that aim to enhance the capabilities of AI in culinary applications, emphasizing the integration of multimodal data and the development of more comprehensive, real- world recipe discovery systems. This work aims to contribute to the growing intersection of AI, food science, and culinary innovation.

Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

BackgroundTo evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes.MethodsA total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance.ResultsFor the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697.ConclusionT1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes.

e18052 {\rtf1\ansi\ansicpg936\cocoartf1347\cocoasubrtf570 {\fonttbl\f0\froman\fcharset0 Times-Roman;\f1\fnil\fcharset0 Calibri;\f2\fswiss\fcharset0 Helvetica; \f3\fnil\fcharset134 STHeitiSC-Light;\f4\froman\fcharset0 TimesNewRomanPSMT;} {\colortbl;\red255\green255\blue255;\red0\green0\blue0;} \paperw11900\paperh16840\margl1440\margr1440\vieww25400\viewh13600\viewkind0 \deftab720 \pard\pardeftab720 \f0\fs24 \cf2 \expnd0\expndtw0\kerning0 \outl0\strokewidth0 \strokec2 AI platform in mammagraphy to screening breast cancer: a sole center experence. \f1\fs21 \cf0 \kerning1\expnd0\expndtw0 \outl0\strokewidth0 \ \pard\pardeftab720\ri714\qj \cf0 Background: The aim of this study was to use deep convolutional neural network (DCNN) to relieve radiologists ' burden and to minimize misses and interpretation errors of suspicious lesions at digital mammography. Methods: We developed and trained the DCNN model on the training set, then validated it in an internal validation set. The labeled data were used for training the DCNN model, and the mass detection, calcification detection and benign and malignant diagnosis were interpreted respectively. After verifying the feasibility of the DCNN model in the diagnosis of breast cancer, we gradually increased the number of cases in the training set and established the verification set to test the training results. The latest training sets include 21100 atlas of our hospital between April 2010 and October 2017, among which the malignant case atlas is 1774. Each atlas contains 2 ipsilateral mammography images. The verification set includes 1307 atlas, among which the malignant case atlas is 248. Results: The DCNN model achieved high performance in identifying breast cancer patients in the validation sets tested, with sensitivity of 98.02%, specificity of 91.86% \f3 \'a3\'ac \f2 and area under the curve values of 0.9813. As for mass detection, we reached a recall rate of 89.6% on the premise of lowing the false positive to 0.168%. For the latest clinical data between October 2018 and November 2018 including 1043 atlas from 576 patients, the sensitivity was 78.6% (95%CI 62.8-89.1) versus 90.5% (95%CI 76.5 \f4 - \f2 96.9; p=0.021) and specificity was 93.4% (95%CI 91.6 \f4 - \f2 94.8) versus 82.7% (95%CI 80.2 \f4 - \f2 85 \f4 . \f2 0; p<0.0001). Conclusions: The DCNN model showed improved specificity in diagnosis breast cancer compared with a group of skilled radiologists. As the number of cases in the training set increases, the capacity of DCNN model will be further improved.