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    Norwegian Institute of Public Health Reports Findings in Artificial Intelligence (Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection)

    41-42页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting originating in Oslo, Norway, by N ewsRx journalists, research stated, “Early breast cancer detection is associated with lower morbidity and mortality. To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the devel opment of future cancer.” The news reporters obtained a quote from the research from the Norwegian Institu te of Public Health, “This retrospective cohort study of 116 495 women aged 50 t o 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2 023) for breast cancer detection and screening data from multiple, consecutive r ounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from Septem ber 2023 to August 2024. Artificial intelligence algorithm score indicating susp icion for the presence of breast cancer. The algorithm provided a continuous can cer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mam mogram. Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative. The mean (SD) age at the firs t study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breas t cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) a t the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, an d 34.0 (33.6) at the third study round. The mean (SD) differences among women wh o did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (1 7.4) at the second study round, and 9.3 (17.3) at the third study round. Areas u nder the receiver operating characteristic curve for the absolute difference wer e 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0 .96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the thi rd study round for interval cancers. In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breas ts developing vs not developing cancer 4 to 6 years before their eventual detect ion.”
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    Zhejiang University Reports Findings in Machine Learning (Machine Learning-Based Toxicological Modeling for Screening Environmental Obesogens)

    43-44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 out of Hangzhou, People’s Rep ublic of China, by NewsRx editors, research stated, “The emerging presence of en vironmental obesogens, chemicals that disrupt energy balance and contribute to a dipogenesis and obesity, has become a major public health challenge. Molecular i nitiating events (MIEs) describe biological outcomes resulting from chemical int eractions with biomolecules.” Our news journalists obtained a quote from the research from Zhejiang University , “Machine learning models based on MIEs can predict complex toxic end points du e to chemical exposure and improve the interpretability of models. In this study , a system was constructed that integrated six MIEs associated with adipogenesis and obesity. This system showed high accuracy in external validation, with an a rea under the receiver operating characteristic curve of 0.78. Molecular hydroph obicity (SlogP_VSA) and direct electrostatic interactions (PEOE_ VSA) were identified as the two most critical molecular descriptors representing the obesogenic potential of chemicals. This system was further used to predict the obesogenic effects of chemicals on the candidate list of substances of very high concern (SVHCs). Results from 3T3-L1 adipogenesis assays verified that the system correctly predicted obesogenic or nonobesogenic effects of 10 of the 12 S VHCs tested, and identified four novel potential obesogens, including 2-benzotri azol-2-yl-4,6-dibutylphenol (UV-320), 4-(1,1,5-trimethylhexyl)phenol (p262-NP), 2-[4-(1,1,3,3-tetramethylbutyl)phenoxy] et hanol (OP1EO) and endosulfan.”
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    Research Conducted at University of Southern California (USC) HasUpdated Our Kn owledge about Machine Learning (The Social Constructionof Datasets: On the Prac tices, Processes, and Challengesof Dataset Creation for Machine Learning)

    45-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting from Los Angeles, Cal ifornia, by NewsRx journalists, research stated, “Despite the critical role that datasets play in how systems make predictions and interpret the world, the dyna mics of their construction are not well understood. Drawing on a corpus of inter views with dataset creators, we uncover the messy and contingent realities of da taset preparation.” Funders for this research include Microsoft, Alfred P. Sloan Foundation.
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    Cracow University of Technology Reports Findings in Colon Cancer (A Novel Approa ch for Predicting the Survival of Colorectal Cancer Patients Using Machine Learn ing Techniques and Advanced Parameter Optimization Methods)

    46-47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 Oncology - Colon Cance r is the subject of a report. According to news originating from Krakow, Poland, by NewsRx correspondents, research stated, “Colorectal cancer is one of the mos t prevalent forms of cancer and is associated with a high mortality rate. Additi onally, an increasing number of adults under 50 are being diagnosed with the dis ease.” Our news journalists obtained a quote from the research from the Cracow Universi ty of Technology, “This underscores the importance of leveraging modern technolo gies, such as artificial intelligence, for early diagnosis and treatment support . Eight classifiers were utilized in this research: Random Forest, XGBoost, CatB oost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset fro m Brazil, containing information on tens of thousands of patients. The models de veloped in this study demonstrated high classification accuracy in predicting on e-, three-, and five-year survival, as well as overall mortality and cancer-spec ific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest cla ssifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks.”
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    Studies in the Area of Robotics Reported from Shanghai Jiao Tong University (Vis uomotor Navigation for Embodied Robots With Spatial Memory and Semantic Reasonin g Cognition)

    47-48页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news reporting originating in Shanghai, People’s R epublic of China, by NewsRx journalists, research stated, “The fundamental prere quisite for embodied agents to make intelligent decisions lies in autonomous cog nition. Typically, agents optimize decision-making by leveraging extensive spati otemporal information from episodic memory.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    Findings in Robotics Reported from Tianjin University (Complete Kinematics/dynam ics Modeling and Performance Analysis of a Novel Scara Parallel Manipulator Base d On Screw Theory)

    50-51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 Robotics are presented i n a new report. According to news reporting from Tianjin, People’s Republic of C hina, by NewsRx journalists, research stated, “In this paper, a novel Selective Compliance Assembly Robot Arm (SCARA) high-speed parallel manipulator that can r ealize three-translation and one-rotation motion is proposed, and an accurate dy namic modeling methodology is investigated. The mechanism is composed of four li mbs with a double parallelogram structure and a single moving platform.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Tianjin Research Innovation Project for Postgraduate Students.
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    Researchers from Southwest University Report on Findings in Computational Intell igence (Robust Hypergraph Regularized Deep Nonnegative Matrix Factorization for Multi-view Clustering)

    53-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing - Computational Intelligence have been published. According to news reportin g originating in Chongqing, People’s Republic of China, by NewsRx journalists, r esearch stated, “As the increasing heterogeneous data, mining valuable informati on from various views is in demand. Currently, deep matrix factorization (DMF) r eceives extensive attention because of its ability to discover latent hierarchic al semantics of the data.” Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Chongqing, Science and Technology Research Program of Chongqing Municipal Education Commission, Open Fund of Key Laboratory of Cyber-Physical Fusion Intelligent Computing (South Central Minzu University) , State Ethnic Affairs Commission.
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    New Computational Intelligence Data Have Been Reported by Investigators at Xidia n University (Multi-graph Contrastive Learning for Community Detection In Multi- layer Networks)

    54-55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 Machine Learning - Comp utational Intelligence is now available. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stat ed, “Multi-layer networks effectively describe and model complex systems in natu re and society, with each layer corresponding to a different type of interaction relationship. Community detection in multi-layer networks aims to identify modu les with strong connectivity in all layers, thus revealing interactions in compl ex systems.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Shaanxi Natural Science Funds for Distinguished Young Schola r Program, Key Research and Development Program of Shaanxi.
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    Findings on Robotics Reported by Investigators at Massachusetts Institute of Tec hnology (clio: Real-time Task-driven Open-set 3d Scene Graphs)

    55-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news originating from Cambridge, Massachusetts, by NewsRx correspondents, research stated, “Modern tools for classagnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. Whil e traditional closed-set metric-semantic maps were restricted to tens or hundred s of semantic classes, we can now build maps with a plethora of objects and coun tless semantic variations.” Financial supporters for this research include National Science Foundation (NSF) , Swiss National Science Foundation (SNSF), MIT Lincoln Laboratory’s Autonomy al Fresco program, ARL DCIST program, ONR RAPID program.
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    Reports Summarize Machine Learning Study Results from Seoul National University College of Medicine (Comparison of NLP machine learning models with human physic ians for ASA Physical Status classification)

    56-57页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from Seoul National Universi ty College of Medicine by NewsRx journalists, research stated, “The American Soc iety of Anesthesiologist’s Physical Status (ASA-PS) classification system assess es comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language p rocessing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summar ies, comparing their performance to human physicians.” Financial supporters for this research include National Research Foundation of K orea (Nrf) Grant Funded By The Korean Government; Seoul National University Hosp ital; New Faculty Startup Fund From Seoul National University.
      原文链接:
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