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machine learning algorithms for predicting future curve using first and second visit data in female adolescent idiopathic scoliosis patients

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EUROPEAN SPINE JOURNAL 2025年 1-9页

作者： Ohyama, Shuhei Maki, Satoshi Kotani, Toshiaki Ogata, Yosuke Sakuma, Tsuyoshi Iijima, Yasushi Akazawa, Tsutomu Inage, Kazuhide Shiga, Yasuhiro Inoue, Masahiro Arai, Takahito Toshi, Noriyasu Tokeshi, Soichiro Okuyama, Kohei Tashiro, Susumu Suzuki, Noritaka Eguchi, Yawara Orita, Sumihisa Minami, Shohei Ohtori, Seiji Chiba Univ Grad Sch Med Dept Orthoped Surg 1-8-1 InohanaChuo Ku Chiba Chiba 2608670 Japan Seirei Sakura Citizen Hosp Dept Orthoped Surg Sakura Japan St Marianna Univ Sch Med Dept Orthopaed Surg Kawasaki Japan Chiba Univ Ctr Frontier Med Engn Chiba Japan

PurposeThis study was designed to develop a machine learning (ML) model that predicts future Cobb angle in patients with adolescent idiopathic scoliosis (AIS) using minimal radiographs and simple questionnaires during the first and second *** study focused on 887 female patients with AIS who were initially consulted at a specialized scoliosis center from July 2011 to February 2023. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first, second, and final visits. ML algorithms were employed to develop individual regression models for future Cobb angles of each curve type (proximal thoracic: PT, main thoracic: MT, and thoracolumbar/lumbar: TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the coefficient of determination (R2) and median absolute error (MAE).ResultsFor the future curve of PT, MT, and TLL, the top-performing models exhibit R2 of 0.73, 0.63, and 0.61 and achieve MAE of 2.3 degrees, 4.0 degrees, and 4.2 *** ML-based model using items commonly evaluated at the first and second visits accurately predicted future Cobb angles in female patients with AIS.

关键词： Adolescent idiopathic scoliosis machine learning algorithms Prediction model Regression model Classification model

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TCC: Time constrained classification of VPN and non-VPN traffic using machine learning algorithms

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WIRELESS NETWORKS 2025年第4期31卷 3415-3429页

作者： Gudla, Raju Vollala, Satyanarayana Srinivasa, K. G. Amin, Ruhul IIIT Naya Raipur CSE Dept Naya Raipur 493661 Chhattisgarh India

Accurate traffic classification plays an important role in efficient utilization of network resources, quality of service, and overall management of the network. The identification of virtual private network (VPN) traffic, in particular, is important since it allows distinguishing between encrypted and non-encrypted traffic by VPN service, which is critical for security monitoring, traffic shaping, and the detection of possible misuse of network resources. VPNs are secure, encrypted connections over an insecure network with predetermined protocols;hence, through traditional methods, it is quite challenging to recognize the traffic pattern. This work introduces the time constrained classification (TCC) model, which use a decision tree classification algorithm with autoencoder dimensionality reduction to extract the key features from encrypted VPN traffic. The TCC model accurately classify VPN traffic from non-VPN traffic without degrading performance and limited amount of time. This approach optimizes the classification time for both binary and multi-class VPN and non-VPN traffic. Experimental results show that the decision tree-based autoencoder model achieves a recall score of 0.993 for multi-class classification in 1.8 s on the UNB ISCX VPN-nonVPN dataset (ISCXVPN2016), outperforming state-of-the-art methods while significantly reducing classification time.

关键词： Network traffic classification machine learning algorithms Dimensionality reduction techniques Binary classification Multi-class classification

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The Application of Supervised machine learning algorithms for Image Alignment in Multi-Channel Imaging Systems

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SENSORS 2025年第2期25卷 544页

作者： Romanenko, Kyrylo Oberemok, Yevgen Syniavskyi, Ivan Bezugla, Natalia Komada, Pawel Bezuglyi, Mykhailo Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Fac Instrumentat Engn Dept Comp Integrated Technol Device Prod Beresteiskyi Ave 37 UA-03056 Kyiv Ukraine Taras Shevchenko Natl Univ Kyiv Dept Quantum Radiophys & Nanoelect 60 Volodymyrska St UA-01033 Kyiv Ukraine Natl Acad Sci Ukraine Main Astron Observ Akad Zabolotnoho St 27 UA-03143 Kyiv Ukraine Lublin Univ Technol Fac Elect Engn & Comp Sci Dept Elect & Informat Tech 38D Nadbystrzycka St PL-20618 Lublin Poland

This study presents a method for aligning the geometric parameters of images in multi-channel imaging systems based on the application of pre-processing methods, machine learning algorithms, and a calibration setup using an array of orderly markers at the nodes of an imaginary grid. According to the proposed method, one channel of the system is used as a reference. The images from the calibration setup in each channel determine the coordinates of the markers, and the displacements of the marker centers in the system's channels relative to the coordinates of the centers in the reference channel are then determined. Correction models are obtained as multiple polynomial regression models based on these displacements. These correction models align the geometric parameters of the images in the system channels before they are used in the calculations. The models are derived once, allowing for geometric calibration of the imaging system. The developed method is applied to align the images in the channels of a module of a multispectral imaging polarimeter. As a result, the standard image alignment error in the polarimeter channels is reduced from 4.8 to 0.5 pixels.

关键词： geometric calibration multi-channel imaging systems image processing image analysis image alignment machine learning algorithms

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Analysis of machine learning algorithms for Real-Time Gallbladder Stone Identification from Ultrasound Images in Clinical Decision Support Systems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-28页

作者： Hong, Chen Zafar, Imran Ayaz, Muhammad Mazhar Kanwal, Rimsha Kanwal, Faheem Dauelbait, Musaab Bourhia, Mohammed Jardan, Yousef A. Bin Hubei Univ Sci & Technol Sch Biomed Engn & Med Imaging Wuhan Peoples R China Univ Faisalabad Fac Sci Dept Biochem & Biotechnol Faisalabad Pakistan Cholistan Univ Vet & Anim Sci Fac Vet Sci Dept Parasitol Bahawalpur Pakistan Univ Lahore Inst Mol Biol & Biotechnol IMBB Lahore Pakistan Univ Bahr El Ghazal Freedowm Stree Wau 91113 Sudan Ibn Zohr Univ Fac Sci Lab Biotechnol & Nat Resources Valorizat Agadir 80060 Morocco King Saud Univ Coll Pharm Dept Pharmaceut POB 11451 Riyadh Saudi Arabia

Global epidemiology of gallstones in the twenty-first century affects millions of individuals, and ultrasound diagnostics effectively assess gallbladder size and function and detect abnormalities. This study collected datasets from local hospitals and reliable online sources for analysis using advanced CV/IP tools and WEKA. Image preprocessing techniques, including cropping, resizing, and grayscale conversion, were applied to 90 ultrasound images, extracting 600 ROIs with 21 features spanning binary, histogram, and texture attributes. The dataset was divided into balanced training and validation subsets, and supervised learning algorithms were optimized via cross-validation and grid search. Circular patterns were processed iteratively, with specific dimensions (512 x 512 for width/height, 32 x 32 for radius/blur, 128 x 128 for columns/rows). The performance of various machine learning classifiers was evaluated using accuracy, precision, recall, F1 score, AUC-ROC, MCC, Kappa GDR, and Dice Index, ensuring strong classification of normal and abnormal samples. The random forest (RF) classifier achieved the highest performance with an accuracy of 96.33%, followed by the MLP and Logit Boost classifiers with 95.67% and 95.40% accuracy rates, respectively. The RF model also exhibited the highest precision (0.9542), recall (0.9732), F1 score (0.9636), and a Dice Index (0.9649) with an MCC of 0.925, ROC area of 0.988, Kappa (0.921), and specificity of 95.34%-indicating its strong ability to balance true positives and negatives while minimizing misclassifications. The MLP classifier also performed well with a precision of 0.9477, a recall of 0.9665, and an F1 score of 0.957, while Logit Boost had similar results with a precision of 0.9411 and a recall of 0.9665. Other classifiers, such as the Bayes Net and J48 classifiers, showed slightly lower performance with accuracy rates of 94.67% but still exhibited good precision and recall, making them viable alternatives. This study h

关键词： Ultrasound imaging Real-time gallbladder stone identification machine learning algorithms Random forest algorithm Clinical decision support systems

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Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis

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BIOMEDICAL ENGINEERING ONLINE 2025年第1期24卷 1-15页

作者： Moannaei, Mehrsa Jadidian, Faezeh Doustmohammadi, Tahereh Kiapasha, Amir Mohammad Bayani, Romina Rahmani, Mohammadreza Jahanbazy, Mohammad Reza Sohrabivafa, Fereshteh Anar, Mahsa Asadi Magsudy, Amin Rafiei, Seyyed Kiarash Sadat Khakpour, Yaser Hormozgan Univ Med Sci Sch Med Bandar Abbas Iran Shahid Beheshti Univ Med Sci Student Res Comm Dept & Fac Hlth Educ & Hlth Promot Tehran Iran Shahid Beheshti Univ Med Sci Student Res Comm Sch Med Tehran Iran Zanjan Univ Med Sci Student Res Comm Zanjan Iran Dezful Univ Med Sci Sch Med Dept Community Med Hlth Educ & Promot Dezful Iran Shahid Beheshti Univ Med Sci Student Res Comm Arabi AveDaneshjoo Blvd Tehran *** Iran Islamic Azad Univ Fac Med Tabriz Branch Tabriz Iran Guilan Univ Med Sci Fac Med Rasht Iran Isfahan Univ Med Sci Student Res Comm Esfahan Iran Shahid Beheshti Univ Med Sci Sch Med Tehran Iran

BackgroundIn recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy. MethodsThis study was conducted based on the PRISMA checklist. We searched online databases, including PubMed, Scopus, and Google Scholar, for relevant articles up to September 30, 2023. After the title, abstract, and full-text screening, data extraction and quality assessment were done for the included studies. Finally, a meta-analysis was performed. ResultsWe included 76 studies with a total of 1,371,517 retinal images, of which 51 were used for meta-analysis. Our meta-analysis showed a significant sensitivity and specificity with a percentage of 90.54 (95%CI [90.42, 90.66], P < 0.001) and 78.33% (95%CI [78.21, 78.45], P < 0.001). However, the AUC (area under curvature) did not statistically differ across studies, but had a significant figure of 0.94 (95% CI [- 46.71, 48.60], P = 1). ConclusionsAlthough machine learning and deep learning algorithms can properly diagnose diabetic retinopathy, their discriminating capacity is limited. However, they could simplify the diagnosing process. Further studies are required to improve algorithms.

关键词： machine learning algorithms Artificial intelligence Diabetic retinopathy Meta-analysis Deep learning

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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms

ARTIFICIAL INTELLIGENCE IN AGRICULTURE

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ARTIFICIAL INTELLIGENCE IN AGRICULTURE 2025年第2期15卷 316-326页

作者： Huang, Guanmin Zhang, Ying Gu, Shenghao Wen, Weiliang Lu, Xianju Guo, Xinyu China Natl Engn Res Ctr Informat Technol Agr Beijing Acad Agr & Forestry Sci Informat Technol Res Ctr Beijing Key Lab Digital Plant Beijing 100097 Peoples R China

Climate change has intensified maize stalk lodging, severely impacting global maize production. While numerous traits influence stalk lodging resistance, their relative importance remains unclear, hindering breeding efforts. This study introduces an combining wind tunnel testing with machine learning algorithms to quantitatively evaluate stalk lodging resistance traits. Through extensive field experiments and literature review, we identified and measured 74 phenotypic traits encompassing plant morphology, biomass, and anatomical characteristics in maize plants. Correlation analysis revealed a median linear correlation coefficient of 0.497 among these traits, with 15.1 % of correlations exceeding 0.8. Principal component analysis showed that the first five components explained 90 % of the total variance, indicating significant trait interactions. Through feature engineering and gradient boosting regression, we developed a high-precision wind speed-ear displacement prediction model (R2 = 0.93) and identified 29 key traits critical for stalk lodging resistance. Sensitivity analysis revealed plant height as the most influential factor (sensitivity coefficient: -3.87), followed by traits of the 7th internode including epidermis layer thickness (0.62), pith area (-0.60), and lignin content (0.35). Our methodological framework not only provides quantitative insights into maize stalk lodging resistance mechanisms but also establishes a systematic approach for trait evaluation. The findings offer practical guidance for breeding programs focused on enhancing stalk lodging resistance and yield stability under climate change conditions, with potential applications in agronomic practice optimization and breeding strategy development. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Maize Stalk lodging Wind tunnel testing machine learning algorithms Indicator identification

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Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms

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APPLIED WATER SCIENCE 2025年第1期15卷 1-22页

作者： Maleky, Sobhan Faraji, Maryam Hashemi, Majid Esfandyari, Akbar Jiroft Univ Med Sci Sch Hlth Dept Environm Hlth Engn Jiroft Iran Kerman Univ Med Sci Environm Hlth Engn Res Ctr Kerman Iran Kerman Univ Med Sci Fac Publ Hlth Dept Environm Hlth Engn Kerman Iran

Assessing water quality is essential for acquiring a better understanding of the importance of water in human society. In this study, the quality of groundwater resources in Jiroft city, Iran, using artificial intelligence methods to estimate the groundwater quality index (GWQI) was evaluated. The analysis of hydrochemical parameters, including arsenic (As), fluoride (F), nitrate (NO3), and nitrite (NO2), in 408 samples revealed that concentrations of F, NO3, and NO2 were below the WHO standard threshold, but levels of As exceeded the permissible value. The random forest model with the highest accuracy (R2 = 0.986) was the best prediction model, while logistic regression (R2 = 0.98), decision tree (R2 = 0.979), K-nearest neighbor (R2 = 0.968), artificial neural network (R2 = 0.955), and support vector machine (R2 = 0.928) predicted GWQI with lower accuracy. The non-carcinogenic risk assessment revealed that children had the highest hazard quotient for oral and dermal intake, with values ranging from 0.47 to 13.53 for oral intake and 0.001 to 0.05 for dermal intake. The excess lifetime cancer risk of arsenic for children, adult females, and males was found to be from 2.5 x 10-4 to 7.2 x 10-3, 1.2 x 10-4 to 3.6 x 10-3, and 4.3 x 10-5 to 1.2 x 10-3, respectively. This study suggests that any effort to reduce the arsenic levels in the Jiroft population should take into account the health hazards associated with exposure to arsenic through drinking water.

关键词： Groundwater quality index machine learning algorithms Health risk assessment Jiroft city

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Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.)

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FOOD CHEMISTRY-X 2025年 27卷 102399页

作者： Wei, Jinpeng Dai, Zhangyan Zhang, Qi Yang, Le Zeng, Zhaoqi Zhou, Yuliang Liu, Jun Chen, Bingxian South China Agr Univ Coll Agr Guangzhou 510642 Peoples R China Guangdong Acad Agr Sci Agrobiol Gene Res Ctr Guangdong Prov Key Lab Crop Germplasm Resources Pr Guangzhou 510640 Peoples R China Zhongkai Univ Agr & Engn Coll Agr & Biol Guangzhou 510550 Peoples R China

Accurate identification of high-quality seeds is crucial for maintaining superior crop traits. Lettuce is widely consumed vegetable with diverse varieties, however, the traditionally identification methods are both timeconsuming and labor-intensive. This study explores feasibility of rapid, non-destructive identification of different lettuce varieties using multispectral imaging combined with machine learning. We firstly collected seed morphological and spectral data from 15 lettuce varieties using multispectral imaging. Then we applied Support Vector machine (SVM), Random Forest (RF), and Back-Propagation Neural Network (BP), Linear Discriminant Analysis (LDA) for variety identification. The results demonstrated that multispectral imaging combined with machine learning models, effectively distinguished different lettuce seed varieties. The LDA model based on morphological and spectral fusion feature data performed best, and the average classification accuracy was 92.7 %. In the batch validation, the LDA model achieved an accuracy of 93.2 %.This method reduces cost and improves efficiency, showing great potential for seed identification in other crops.

关键词： Lettuce Variety identification Seed multispectral imaging machine learning algorithms

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Detection of Water Surface Using Canny and Otsu Threshold Methods with machine learning algorithms on Google Earth Engine: A Case Study of Lake Van

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APPLIED SCIENCES-BASEL 2025年第6期15卷

作者： Karakus, Pinar Osmaniye Korkut Ata Univ Engn & Nat Sci Fac TR-80000 Osmaniye Turkiye

Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f s

关键词： Google Earth Engine Canny edge detection Otsu threshold NDWI AWEI MNDWI machine learning algorithms

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Research on virtual entertainment robots based on machine learning algorithms providing psychological health services for college students

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Ma, Xiao Chongqing Business Vocat Coll Sch Accounting Shapingba 401331 Chongqing Peoples R China

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.

关键词： machine learning algorithms Virtual entertainment robots College students Psychological health services

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