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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 544-544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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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 2903-2903页

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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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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Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of machine learning algorithms with Microfluidic Technologies

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2025年第4期15卷 220-220页

作者： An, Ling Liu, Yi Liu, Yaling Dali Univ Sch Engn Dali 671003 Peoples R China Sichuan Univ West China Hosp Precis Med Translat Res Ctr Chengdu 610041 Peoples R China Lehigh Univ Dept Bioengn Bethlehem PA 18015 USA

Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant challenges for detection. Microfluidic systems, or "lab-on-a-chip" devices, have emerged as a revolutionary tool in liquid biopsy, enabling efficient isolation and analysis of CTCs. These systems offer advantages such as reduced sample volume, enhanced sensitivity, and the ability to integrate multiple processes into a single platform. Several microfluidic techniques, including size-based filtration, dielectrophoresis, and immunoaffinity capture, have been developed to enhance CTC detection. The integration of machine learning (ML) with microfluidic systems has further improved the specificity and accuracy of CTC detection, significantly advancing the speed and efficiency of early cancer diagnosis. ML models have enabled more precise analysis of CTCs by automating detection processes and enhancing the ability to identify rare and heterogeneous cell populations. These advancements have already demonstrated their potential in improving diagnostic accuracy and enabling more personalized treatment approaches. In this review, we highlight the latest progress in the integration of microfluidic technologies and ML algorithms, emphasizing how their combination has changed early cancer diagnosis and contributed to significant advancements in this field.

关键词： circulating tumor cells (CTCs) microfluidic systems machine learning algorithms early cancer diagnosis

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Non-enzymatic electrochemical sensors using Polyaniline:Metal orotate nanocomposites for selective dopamine and glucose detection: Predicting sensor performance with machine learning algorithms

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MATERIALS SCIENCE IN SEMICONDUCTOR PROCESSING 2025年 193卷

作者： Yildiz, Dilber Esra Tasaltin, Nevin Baytemir, Guelsen Gursu, Gamze Karakus, Selcan Yildirim, Tugrul Sahin, Yesim Muge Kucukdeniz, Tarik Kose, Dursun Ali Hitit Univ Dept Phys Corum Turkiye Maltepe Univ Environm & Energy Technol Res Ctr Istanbul Turkiye Maltepe Univ Dept Renewable Energy Tech & Management Istanbul Turkiye Maltepe Univ Dept Basic Sci Istanbul Turkiye Maltepe Univ CONSENS Inc Technopark Istanbul Res Ctr Istanbul Turkiye Maltepe Univ Dept Elect & Elect Engn Istanbul Turkiye Istanbul Univ Cerrahpasa Dept Chem Istanbul Turkiye Hitit Univ Vocat Sch Tech Sci Occupat Hlth & Safety Program TR-19030 Corum Turkiye Arel Univ Arel Potkam Istanbul Turkiye Hlth Biotechnol Joint Res & Applicat Ctr Excellenc TR-34220 Istanbul Turkiye Istanbul Univ Cerrahpasa Fac Engn Dept Ind Engn TR-34320 Istanbul Turkiye Hitit Univ Dept Chem Corum Turkiye

In this study, we developed novel non-enzymatic electrochemical biosensors, capitalizing on the capabilities of orotate metal complexes encompassing Co(II), Cu(II), Ni(II), and Zn(II) cations coordinated within polyaniline (PANI) nanocomposites (NCs). Our approach involved the meticulous crystallization of metal cation complexes from solution, utilizing orotic acid as a ligand. Through the utilization of a cost-efficient and uncomplicated sonication technique, we synthesized PANI:Co(II), PANI:Ni(II), PANI:Cu(II), and PANI:Zn(II) orotate NCs. Our results demonstrated that the PANI:Zn(II) orotate NCs-based sensor exhibited the highest sensitivity, recording 99.25 mu A cm- 2 mu M- 1 for dopamine detection, with a remarkable limit of detection (LOD) of 0.74 mu M. Furthermore, the sensor effectively detected glucose with a sensitivity of 24.39 mu Acm- 2mM-1 and a LOD of 1.89 mM. Importantly, the sensor displayed exceptional selectivity towards dopamine. To further enhance the sensor performance, machine learning algorithms were applied to analyze and predict the sensor's output. Models such as Linear Regression and Artificial Neural Networks (ANN) were employed to interpret the electrochemical results and predict performance metrics like sensitivity and selectivity. The outcomes of our sensor assessments underscore the potential of the PANI:Zn(II) orotate NCs as a robust platform for dopamine detection applications. Our findings provide insights for improving PANI: Zn(II) orotate NCs in biosensing. PANI:metal orotate NCs function as semiconductor materials, with sensor sensitivity closely tied to their conductance, conductivity, and impedance.

关键词： Orotate Semiconductor materials machine learning algorithms Dopamine Glucose

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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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