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    Nanjing University Reports Findings in Artificial Intelligence [Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL St udy)-a protocol for a ...]

    86-87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 Artificial Intelligenc e is the subject of a report. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, "This multicenter, d ouble-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneury sm detection in CT angiography (CTA) and its influence on patients' short-term a nd long-term outcomes. Prospective, multicenter, double-blinded RCT." Financial supporters for this research include National Natural Science Foundati on of China, Key Programme. The news correspondents obtained a quote from the research from Nanjing Universi ty, "The model was designed for the automatic detection of intracranial aneurysm s from original CTA images. Adult inpatients and outpatients who are scheduled f or head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA int erpreted by radiologists with the assistance of the True-AI-integrated intracran ial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA inter preted by radiologists with the assistance of the Sham-AI-integrated intracrania l aneurysm diagnosis strategy (Sham-AI arm). Block randomization, stratified by center, gender, and age group. Coprimary outcomes of superiority in patient-leve l sensitivity and noninferiority in specificity for the True-AI arm to the Sham- AI arm in intracranial aneurysms. Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis. Study participants and participating radiologists will be blinded to the intervention. Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intrac ranial aneurysms for patients undergoing head CTA in the hospital is approximate ly 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided a = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm. The study will determine the precise impact of the AI s ystem on the detection performance for intracranial aneurysms in a double-blinde d design and following the real-world effects on patients' short-term and long-t erm outcomes. This trial has been registered with the NIH, U.S."
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    First Affiliated Hospital of Harbin Medical University Reports Findings in Adeno carcinoma (Evaluating the predictive value of angiogenesis-related genes for pro gnosis and immunotherapy response in prostate adenocarcinoma using machine learn ing ...)

    87-88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 - Adenocarcin oma is the subject of a report. According to news reporting originating in Harbi n, People's Republic of China, by NewsRx journalists, research stated, "Angiogen esis, the process of forming new blood vessels from pre-existing ones, plays a c rucial role in the development and advancement of cancer. Although blocking angi ogenesis has shown success in treating different types of solid tumors, its rele vance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated." The news reporters obtained a quote from the research from the First Affiliated Hospital of Harbin Medical University, "This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was co nstructed using multiple machine learning techniques, while a prognostic model w as developed employing the LASSO algorithm, underscoring the relevance of angiog enesis-related genes in PRAD. Further analysis identified MAP7D3 as the most sig nificant prognostic gene among angiogenesisrelated genes using multivariate Cox regression analysis and various machine learning algorithms. The study also inv estigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the bin ding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3. Ov erall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD an d its response to immunotherapy. Through molecular docking studies, it was revea led that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermor e, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating w ith a poorer prognosis."
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    Studies from University of Djelfa Describe New Findings in Robotics (Robotic Vis ual-based Navigation Structures Using Lucas-kanade and Horn-schunck Algorithms o f Optical Flow)

    88-89页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Robotics. Acc ording to news reporting out of Djelfa, Algeria, by NewsRx editors, research sta ted, "This paper aims to present vision-based navigation structures for a wheele d mobile robot using optical flow techniques. The two algorithms of the differen tial approach are examined and investigated for visual motion in unknown static and dynamic indoor environments." Our news journalists obtained a quote from the research from the University of D jelfa, "Horn-Schunck (HS) and Lucas-Kanade (LK) algorithms of the optical flow ( OF) technique are employed to extract information about the environment surround ing the controlled robot by an installed color camera on the robot platform. Obs tacles and objects are identified and detected based on image processing and vid eo acquisition steps for the different tasks of mobile robots: navigation of one robot with static obstacle avoidance, navigation with dynamic obstacle avoidanc e, and multi-robot navigation with a static obstacle. The proposed control struc tures are based on motion estimation and decision mechanisms that use the necess ary measured variables calculated by optical flow algorithms to carry out the ap propriate steering actions to guide autonomously the robot in its workspace. The efficiency of the proposed control structures is tested in 2D and 3D environmen ts using the Virtual Reality Modeling Language (VRML) Toolbox of Matlab."
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    Findings from University College London (UCL) Has Provided New Data on Robotics (Efficient Global Navigational Planning In 3-d Structures Based On Point Cloud T omography)

    89-90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 Ro botics. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, "Navigation in complex 3-D scenarios req uires appropriate environment representation for efficient scene understanding a nd trajectory generation. We propose a highly efficient and extensible global na vigation framework based on a tomographic understanding of the environment to na vigate ground robots in multilayer structures." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from University College Lond on (UCL), "Our approach generates tomogram slices using the point cloud map to e ncode the geometric structure as ground and ceiling elevations. Then, it evaluat es the scene traversability considering the robot's motion capabilities. Both th e tomogram construction and the scene evaluation are accelerated through paralle l computation. Our approach further alleviates the trajectory generation complex ity compared with planning in 3-D spaces directly. It generates 3-D trajectories by searching through multiple tomogram slices and separately adjusts the robot height to avoid overhangs. We evaluate our framework in various simulation scena rios and further test it in the real world on a quadrupedal robot."
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    Research from Chinese Academy of Sciences Provides New Study Findings on Machine Learning (Potential of Sample Migration and Explainable Machine Learning Model for Monitoring Spatiotemporal Changes of Wetland Plant Communities)

    90-91页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting out of Changchun, People's Republic of China, by NewsRx editors, research stated, "The composition and dyna mics of wetland plant communities play a critical role in maintaining the functi onality of wetland ecosystems and serve as important indicators of wetland degra dation and restoration." Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Jilin Province. Our news editors obtained a quote from the research from Chinese Academy of Scie nces: "Accurately identifying wetland plant communities using remote sensing tec hniques remains challenging due to the complex environment and cloud contaminati on. Here, we applied a sample migration method based on change vector analysis a nd a random forest (RF) classifier incorporating SHapley Additive exPlanations ( SHAP) to explore the spatiotemporal changes of wetland plant communities in the western Songnen Plain of China between 2016 and 2022, and to better understand t he decision logic of the RF model. Our work achieved accurate annual wetland cla ssification at the community scale, with an average overall accuracy of 89.5% and an average kappa coefficient of 0.87. Our analysis revealed different spatio temporal change characteristics of wetland plant communities in the western Song nen Plain and three national nature reserves. The SHAP model showed that MOS_ IRECI is the most important feature determining the prediction results of the RF model, and the importance of the features differs at global and local levels. T his study confirms the feasibility of annual dynamic monitoring of wetland plant communities at a regional scale."
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    New Findings from Mayo Clinic Update Understanding of Machine Learning (An Integ rated Voice Recognition and Natural Language Processing Platform To Automaticall y Extract Thoracolumbar Injury Classification Score Features From Radiology Repo rts)

    91-92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 have been published. According to news reporting originating from Rochester, Minnesota, by NewsRx correspondents, research stated, "Many predictive models f or estimating clinical outcomes after spine surgery have been reported in the li terature. However, implementation of predictive scores in practice is limited by the time -intensive nature of manually abstracting relevant predictors." Financial support for this research came from Clinical Translational Science Awa rds from the National Center for Advancing Translational Science.
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    Studies from Carnegie Mellon University Further Understanding of Machine Learnin g (Surface Segregation Studies In Ternary Noble Metal Alloys: Comparing Dft and Machine Learning With Experimental Data)

    92-93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 Machine Learning have been presented. According to news reporting from Pittsburgh, Pennsylvania, by N ewsRx journalists, research stated, "Surface segregation, whereby the surface co mposition of an alloy differs systematically from the bulk, has historically bee n hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalyti cally relevant noble and platinum-group metal alloys with a focus on three terna ry systems: AgAuCu, AuCuPd, and CuPdPt." Financial supporters for this research include National Energy Research Scientif ic Computing Center (NERSC), United States Department of Energy (DOE), NSF DMREF Award.
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    Research on Machine Learning Published by a Researcher at Jilin University (Mapp ing Topsoil Carbon Storage Dynamics of Croplands Based on Temporal Mosaicking Im ages of Landsat and Machine Learning Approach)

    93-94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 out of Changchun, People's Republ ic of China, by NewsRx editors, research stated, "Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a v ital prerequisite in decision-making for maintaining sustainable agriculture. Ho wever, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved." Funders for this research include National Key Research And Development Program of China; National Natural Science Foundation of China.
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    Findings on Artificial Intelligence Discussed by Investigators at University of Western Australia (Defining Intelligence: Bridging the Gap Between Human and Art ificial Perspectives)

    94-95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 Perth, Australia, by Ne wsRx journalists, research stated, "Achieving a widely accepted definition of hu man intelligence has been challenging, a situation mirrored by the diverse defin itions of artificial intelligence in computer science. By critically examining p ublished definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines." The news correspondents obtained a quote from the research from the University o f Western Australia, "Abstract and operational definitions for human and artific ial intelligence are proposed that emphasize maximal capacity for completing nov el goals successfully through respective perceptual-cognitive and computational processes. Additionally, support for considering intelligence, both human and ar tificial, as consistent with a multidimensional model of capabilities is provide d. The implications of current practices in artificial intelligence training and testing are also described, as they can be expected to lead to artificial achie vement or expertise rather than artificial intelligence. Paralleling psychometri cs, ‘AI metrics' is suggested as a needed computer science discipline that ackno wledges the importance of test reliability and validity, as well as standardized measurement procedures in artificial system evaluations. Drawing parallels with human general intelligence, artificial general intelligence (AGI) is described as a reflection of the shared variance in artificial system performances. We con clude that current evidence more greatly supports the observation of artificial achievement and expertise over artificial intelligence."
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    University of Leuven (KU Leuven) Reports Findings in Machine Learning (Machine-L earning Approaches for Risk Prediction in Transcatheter Aortic Valve Implantatio n: Systematic Review and Meta-Analysis)

    95-96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 from Leuven, Belgium, by News Rx journalists, research stated, "With the expanding integration of artificial i ntelligence (AI) and machine learning (ML) into the structural heart domain, num erous ML models have emerged for the prediction of adverse outcomes following tr anscatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI." The news correspondents obtained a quote from the research from the University o f Leuven (KU Leuven), "Key objectives consisted in summarizing model performance , evaluating adherence to reporting guidelines, and transparency. We searched Pu bMed, SCOPUS, and Embase through August 2023. We selected published machine lear ning models predicting TAVI outcomes. Two reviewers independently screened artic les, extracted data, and assessed the study quality according to the PRISMA guid elines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Out comes included summary C-statistics and model risk of bias assessed with the Pre diction Model Risk of Bias Assessment Tool (PROBAST). C-statistics were pooled u sing a random-effects model. Twenty-one studies (118,153 patients) employing var ious ML algorithms (76 models) were included in the systematic review. Predictiv e ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60-0.70), 31.6% acceptable (C-statistic 0.70-0.80), and 30.3% demonstrated excelle nt (C-statistic >0.80) performance. Meta-analyses reveal ed excellent predictive performance for early mortality (C-statistic: 0.81 [95 % CI, 0.65-0.91]), acceptable performance for 1-year mortality (C-statistic: 0.76 [95% CI, 0 .67-0.84]), and acceptable performance for predicting permane nt pacemaker implantation (C-statistic: 0.75 [95% CI, 0.51-0.90]). ML models for TAVI outcomes exhibit adequate to excellent performance, suggesting potential clinical utility."
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