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    Institute of Computational Biology Reports Findings in Machine Learning (Machine learning integrative approaches to advance computational immunology)

    20-20页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Munich, German y, by NewsRx journalists, research stated, "The study of immunology, traditional ly reliant on proteomics to evaluate individual immune cells, has been revolutio nized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker ident ification to encompass a more detailed view of cellular phenotypes and their fun ctional roles." Financial support for this research came from Helmholtz Zentrum Munchen - Deutsc hes Forschungszentrum fur Gesundheit und Umwelt (GmbH).

    Southeast University Reports Findings in Maxillary Sinusitis (Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis)

    21-21页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Nose Diseases and Cond itions - Maxillary Sinusitis is the subject of a report. According to news origi nating from Nanjing, People's Republic of China, by NewsRx correspondents, resea rch stated, "To construct the deep learning convolution neural network (CNN) mod el and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accurac y of image diagnosis. Maxillary sinus CT data of 1000 samples in 500 patients fr om January 2018 to December 2021 in our hospital was collected." Financial support for this research came from Zigang Che. Our news journalists obtained a quote from the research from Southeast Universit y, "The first part is the establishment and testing of chronic maxillary sinusit is detection model by 461 images. The second part is the establishment and testi ng of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively. Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 sam ples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the v alue of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, r espectively. Simultaneously, the value of AUC was 0.89."

    Data on Endometriosis Reported by Gianmarco D' Ancona and Colleagues (Robotic-as sisted laparoscopy excision of a severe form of diaphragmatic endometriosis: a r etrospective study of 60 patients)

    22-22页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Uterine Diseases and C onditions - Endometriosis is the subject of a report. According to news reportin g originating from Bordeaux, France, by NewsRx correspondents, research stated, "To assess the feasibility, effectiveness and safety of the robotic surgical app roach in the treatment of severe diaphragmatic endometriosis (DE). Retrospective single-center study using data prospectively recorded in the Franco-European Mu ltidisciplinary Institute of Endometriosis (IFEMendo) database and National obse rvatory for endometriosis (NoEndo) database. Tertiary referral center. Endometri osis care center." Our news editors obtained a quote from the research, "Sixty consecutive patients undergoing robotic excision of severe DE from January 2020 to July 2023. Roboti c excision of severe DE. Categorical and continuous variables were evaluated and compared using descriptive statistics. A p-value of <0.05 was considered statistically significant. Full thickness diaphragmatic resection was performed in 76.7% of patients (46/60), partial diaphragmatic muscle resection in 10% (6/60) of cases. Peritoneal stripping tec hnique was performed in 60% (36/60) of patients, divided as follow s: as the only technique in case of extensive superficial diaphragmatic involvem ent in 13.3% of cases (8/60); in addition to full-thickness or par tial diaphragmatic resection in case of concomitant multiple foci in 46.7% of patients (28/60). Median operative time was 79.6 minutes with no statisticall y significative difference related to the surgeon performing surgery (p > 0.05). Intraoperative and postoperative complications occurred in 1.7% (1/60) and 6.6% (4/60) of cases, respectively. Diaphragmatic herni a (Clavien-Dindo 3b) was the most common postoperative complication and required surgical repair in all cases. Median hospital stay was 24 hours. The rate of pa tients with complete recovery from DE symptoms has gradually increased during fo llow-up, reaching 89% after 12 months from surgery. In this case s eries, robotic treatment of severe diaphragmatic endometriosis in expert hands w as feasible, effective and safe."

    Researcher from National University Colombia Describes Findings in Machine Learn ing (Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques)

    23-24页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Medellin, Colombia, by NewsRx correspondents, research stated, "The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conduc ted by The Organization for Economic Co-operation and Development." The news journalists obtained a quote from the research from National University Colombia: "This study compares five feature selection techniques to address thi s challenge and improve the automatic detection of dropout risk. The methodologi cal design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms."

    New Machine Learning Study Results Reported from Tel Aviv Medical Center (A mach ine learning contest enhances automated freezing of gait detection and reveals t ime-of-day effects)

    23-23页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from the Tel Aviv Medi cal Center by NewsRx correspondents, research stated, "Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 3 8-65% of people with Parkinson's disease." Financial supporters for this research include Michael J. Fox Foundation For Par kinson's Research. Our news correspondents obtained a quote from the research from Tel Aviv Medical Center: "During a FOG episode, patients report that their feet are suddenly and inexplicably ‘glued' to the floor. The lack of a widely applicable, objective F OG detection method obstructs research and treatment. To address this problem, w e organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams fr om 83 countries submitted 24,862 solutions. The winning solutions demonstrated h igh accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occur rences."

    Data on Geotechnical Engineering Detailed by Researchers at Brunel University Lo ndon (A Machine Learning-assisted Nondestructive Testing Method Based On Time-do main Wave Signals)

    24-25页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on En gineering - Geotechnical Engineering. According to news reporting originating fr om London, United Kingdom, by NewsRx correspondents, research stated, "The nonde structive testing (NDT) of defects in structure members or geological bodies is essential in engineering survey and construction. Given the complexity of natura l rock or rock masses, the NDT of defects within them is extremely challenging i n geotechnical engineering." Financial supporters for this research include Rock Dynamic Laboratory in Southe ast University-Monash University Joint Graduate School, National Natural Science Foundation of China (NSFC), State Key Laboratory for GeoMechanics and Deep Unde rground Engineering, China University of Mining Technology.

    Study Findings from University of Toulouse Update Knowledge in Evolutionary Comp utation (Multiobjective Evolutionary Component Effect on Algorithm Behaviour)

    25-26页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on evolutionary computation have been published. According to news reporting out of Toulouse, France, by Ne wsRx editors, research stated, "The performance of multiobjective evolutionary a lgorithms (MOEAs) varies across problems, making it hard to develop new algorith ms or apply existing ones to new problems. To simplify the development and appli cation of new multiobjective algorithms, there has been an increasing interest i n their automatic design from their components." The news editors obtained a quote from the research from University of Toulouse: "These automatically designed metaheuristics can outperform their human-develop ed counterparts. However, it is still unknown what are the most influential comp onents that lead to performance improvements. This study specifies a new methodo logy to investigate the effects of the final configuration of an automatically d esigned algorithm. We apply this methodology to a tuned Multiobjective Evolution ary Algorithm based on Decomposition (MOEA/D) designed by the iterated racing (i race) configuration package on constrained problems of 3 groups: (1) analytical real-world problems, (2) analytical artificial problems and (3) simulated real-w orld. We then compare the impact of the algorithm components in terms of their S earch Trajectory Networks (STNs), the diversity of the population, and the anyti me hypervolume values. Looking at the objective space behavior, the MOEAs studie d converged before half of the search to generally good HV values in the analyti cal artificial problems and the analytical real-world problems."

    Huazhong University of Science and Technology Reports Findings in Pulmonary Embo lism (Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study)

    26-27页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Lung Diseases and Cond itions - Pulmonary Embolism is the subject of a report. According to news report ing out of Hubei, People's Republic of China, by NewsRx editors, research stated , "Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with hi gh mortality among patients with autoimmune inflammatory rheumatic diseases (AII RDs). Accurate prediction and timely intervention play a pivotal role in enhanci ng survival rates." Our news journalists obtained a quote from the research from the Huazhong Univer sity of Science and Technology, "However, there is a notable scarcity of practic al early prediction and risk assessment systems of PE in patients with AIIRD. In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease- matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospi tal from 2014 to 2022. Univariable logistic regression (LR) and least absolute s hrinkage and selection operator (LASSO) were used to select the clinical feature s for further training with machine learning (ML) methods,including random fore st (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression tree s (CART), and C5.0 models. The performances of these models were subsequently va lidated using a multicenter validation cohort. In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respect ively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performan ces, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the trai ning cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achi eved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer level s alone."

    New Machine Learning Study Results Reported from University Putra Malaysia (The impact of the combat method on radiomics feature compensation and analysis of sc anners from different manufacturers)

    27-28页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from the University Putra Malaysi a by NewsRx journalists, research stated, "This study investigated whether the C ombat compensation method can remove the variability of radiomic features extrac ted from different scanners, while also examining its impact on the subsequent p redictive performance of machine learning models. 135 CT images of Credence Cart ridge Radiomic phantoms were collected and screened from three scanners manufact ured by Siemens, Philips, and GE." Funders for this research include Chengde Medical University Project; Hebei Prov ince Introduced Returned Overseas Chinese Scholars Funding Project; Chengde Biom edicine Industry Research Institute Funding Project.

    UiT The Arctic University of Norway Researcher Reports Research in Artificial In telligence (Artificial Intelligence-Driven Innovations in Hydrogen Safety)

    28-29页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news originating from Narvik, Norway , by NewsRx editors, the research stated, "This review explores recent advanceme nts in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques." Funders for this research include Uit The Arctic University of Norway. Our news editors obtained a quote from the research from UiT The Arctic Universi ty of Norway: "As hydrogen gains prominence as a clean energy source, ensuring i ts safe handling becomes paramount. The paper critically evaluates the implement ation of AI methodologies, including artificial neural networks (ANN), machine l earning algorithms, computer vision (CV), and data fusion techniques, in enhanci ng hydrogen safety measures. By examining the integration of wireless sensor net works and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transf ormative potential of AI in revolutionizing safety frameworks. Moreover, it addr esses key challenges such as the scarcity of standardized datasets, the optimiza tion of AI models for diverse environmental conditions, etc., while also identif ying opportunities for further research and development."