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    Capital Medical University Reports Findings in Hepatitis B Virus (Machine learni ng-based model for predicting tumor recurrence after interventional therapy in H BV-related hepatocellular carcinoma patients with low preoperative ...)

    69-70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 Liver Diseases and Con ditions - Hepatitis B Virus is the subject of a report. According to news report ing from Beijing, People's Republic of China, by NewsRx journalists, research st ated, "This study aimed to develop a prognostic nomogram for predicting the recu rrencefree survival (RFS) of hepatitis B virus (HBV)-related hepatocellular car cinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) s cores after transarterial chemoembolization (TACE) combined with local ablation treatment. We gathered clinical data from 632 HBV-related HCC patients who recei ved the combination treatment at Beijing You'an Hospital, affiliated with Capita l Medical University, from January 2014 to January 2020." Financial support for this research came from Bethune Charitable Foundation.
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    Findings on Artificial Intelligence Reported by Investigators at Near East Unive rsity (Artificial Intelligence-based Algorithm for Cervical Vertebrae Maturation Stage Assessment)

    70-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on Artificial Intelligence have bee n presented. According to news reporting originating from Mersin, Turkey, by New sRx correspondents, research stated, "The aim of this study was to develop an ar tificial intelligence (AI) algorithm to automatically and accurately determine t he stage of cervical vertebra maturation (CVM) with the main purpose being to el iminate the human error factor. Setting and Sample Population Archives of the ce phalometric images were reviewed and the data of 1501 subjects with fully visibl e cervical vertebras were included in this retrospective study." Our news editors obtained a quote from the research from Near East University, " Lateral cephalometric (LC) that met the inclusion criteria were used in the trai ning process, labeling was carried out using a computer vision annotation tool ( CVAT), tracing was done by an experienced orthodontist as a gold standard and, i n order to limit the effect of the uneven distribution of the training data set, maturation stage was classified with a modified Bachetti method by the operator who labelled them. The labelled data were split randomly into a training set (8 0%), a testing set (10%) and an validation set (10% ), to measure intra-observer, inter-observer reliability, intraclass correlation coefficient (ICC) and weighted Cohen's kappa test was carried out. The ICC was valued at 0.973, weighted Cohen's kappa standard error was 0.870 +/- 0.027 which shows high reliability of the observers and excellent level of agreement betwee n them, the segmentation network achieved a global accuracy of 0.99 and the aver age dice score overall images was 0.93. The classification network achieved an a ccuracy of 0.802, class sensitivity of (pre-pubertal 0.78; pubertal 0.45; post-p ubertal 0.98), respectively, per class specificity of (pre-pubertal 0.94; pubert al 0.94; post-pubertal 0.75), respectively. The developed algorithm showed the a bility to determine the cervical vertebrae maturation stage which might aid in a faster diagnosis process by eliminating human intervention, which might lead to wrong decision-making procedures that might affect the outcome of the treatment plan."
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    Researchers from Norwegian University of Science and Technology (NTNU) Describe Findings in Robotics (Visual Sensing On Marine Robotics for the 3d Documentation of Underwater Cultural Heritage: a Review)

    71-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Robotic s. According to news reporting originating in Trondheim, Norway, by NewsRx journ alists, research stated, "This study provides a comprehensive review of the curr ent state of the art in marine technology as it pertains to the 3D documentation of underwater archaeological and historical sites. A thorough literature analys is of recent research is presented, with a special emphasis on vision-based appr oaches for 3D reconstruction and mapping." Financial support for this research came from Research Council of Norway. The news reporters obtained a quote from the research from the Norwegian Univers ity of Science and Technology (NTNU), "First, the paper lists different robotic platforms, various underwater imaging systems and possible combinations among th em, through their use in marine archaeological research. In addition to robotic vision systems configurations, a thorough survey on computer vision solutions on image processing, online and offline reconstructions, for both simulation envir onments and real-world UCH scenarios, is given. The final part of the paper revi ews strategies for data acquisition optimization through path planning approache s and highlights how working on synthetic data and simulation environments can e nhance the quality of real-world operations."
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    Research in the Area of Artificial Intelligence Reported from Universidad de Gua yaquil (An analysis of Leadership and proactivity to face the challenges of indu stry 5.0)

    72-72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 reporting from the Universidad de Guayaquil by NewsRx journalists, research stated, "Industry 5.0 is a new tech nological revolution accompanied by a process of changes as it aims to enhance t he transformation of the industrial sector into intelligent spaces based on the Internet of Things, artificial intelligence, and robotics, among others." Our news reporters obtained a quote from the research from Universidad de Guayaq uil: "For this reason, industry 5.0 involves challenges, one of them is the know ledge of people in the fields of robotics and artificial intelligence to achieve adequate interaction between machines and operators. Therefore, the organizatio n's responsibility is based on training employees in virtual education, in addit ion to safe training that can prevent employees from going through unnecessary p roblems during training sessions, in addition to enhancing communication and mot ivation of employees to obtain interactive knowledge environments [1] and thus can better adapt to these changes; which is why t he need arises to find an appropriate leadership style that develops self-initia ted behaviors in employees to carry out processes, autonomy to make decisions an d confidence to face challenges."
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    Investigators from Beijing Normal University Report New Data on Machine Learning (Multi-fidelity Machine Learning for Predicting Bandgaps of Nonlinear Optical C rystals)

    73-73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 Ma chine Learning. According to news reporting originating in Beijing, People's Rep ublic of China, by NewsRx journalists, research stated, "Nonlinear optical (NLO) materials are of great importance in modern optics and industry because of thei r intrinsic capability of wavelength conversion. Bandgap is a key property of NL O crystals." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province, Beijing Normal University Startup. The news reporters obtained a quote from the research from Beijing Normal Univer sity, "In recent years, machine learning (ML) has become a powerful tool to pred ict the bandgaps of compounds before synthesis. However, the shortage of availab le experimental data of NLO crystals poses a significant challenge for the explo ration of new NLO materials using ML. In this work, we proposed a new multi-fide lity ML approach based on the multilevel descriptors developed by us (Z.-Y. Zhan g, X. Liu, L. Shen, L. Chen and W.-H. Fang, J. Phys. Chem. C, 2021, 125, 25175-2 5188) and the gradient boosting regression tree algorithm. The calculated and ex perimental bandgaps of NLO crystals were collected as the low- and high-fidelity labels, respectively. The experimental values were predicted based on chemical compositions of crystals without prior knowledge about crystal structures. The m ulti-fidelity ML model overcame the performance of single-fidelity predictor. Fu rthermore, it was observed that less accurate predictions on the low-fidelity la bel may result in more accurate prediction on the high-fidelity label, at least in the present case. Using the multi-fidelity ML model with the best performance in this work, the mean absolute error on the test set of experimental bandgaps was 0.293 eV, which is smaller than that using the single-fidelity model (0.355 eV). It is far from perfect but accurate enough as an effective computational to ol in the first step to discover novel NLO materials."
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    Study Data from Halmstad University Provide New Insights into Artificial Intelli gence (Towards evidence-based practice 2.0: leveraging artificial intelligence i n healthcare)

    74-75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 Halmstad, Sweden, by N ewsRx editors, the research stated, "BackgroundEvidence-based practice (EBP) inv olves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, res earch has shown that there are many barriers to achieving the goals of the EBP m odel. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making." Our news editors obtained a quote from the research from Halmstad University: "T he aim of this paper was to pinpoint key challenges pertaining to the three pill ars of EBP and to investigate the potential of AI in surmounting these challenge s and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcar e to achieve this. Challenges with the three components of EBPClinical decision- making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generati on and dissemination processes, as well as the scarcity of high-quality evidence . Direct application of evidence is not always viable because studies often invo lve patient groups distinct from those encountered in routine healthcare. Clinic ians need to rely on their clinical experience to interpret the relevance of evi dence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. A chieving patient involvement and shared decision-making between clinicians and p atients remains challenging in routine healthcare practice due to factors such a s low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patien t knowledge and ineffective communication strategies, busy healthcare environmen ts and limited resources. AI assistance for the three components of EBPAI presen ts a promising solution to address several challenges inherent in the research p rocess, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinician s in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential t o increase patient autonomy although there is a lack of research on this issue. ConclusionThis review underscores AI's potential to augment evidence-based healt hcare practices, potentially marking the emergence of EBP 2.0."
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    Investigators at Chang'an University Report Findings in Robotics (Dynamic Compli ance of Energy-saving Legged Elastic Parallel Joints for Quadruped Robots: Desig n and Realization)

    75-75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 from Xi'an, People's Re public of China, by NewsRx correspondents, research stated, "Achieving dynamic c ompliance for energy-efficient legged robot motion is a longstanding challenge. Although recent predictive control methods based on single-rigid-body models can generate dynamic motion, they all assume infinite energy, making them unsuitabl e for prolonged robot operation." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), State Key Laboratory of Robotics and Systems, Harbin Institu te of Technology, China, Fundamental Research Funds for Central Universities, Ch ina. Our news editors obtained a quote from the research from Chang'an University, "A ddressing this issue necessitates a mechanical structure with energy storage and a dynamic control strategy that incorporates feedback to ensure stability. This work draws inspiration from the efficiency of bio-inspired muscle-tendon networ ks and proposes a controllable torsion spring leg structure. The design integrat es a spring-loaded inverted pendulum model and adopts feedback delays and yield springs to enhance the delay effects. A leg control model that incorporates moto r loads is developed to validate the response and dynamic performance of a leg w ith elastic joints. This model provides torque to the knee joint, effectively re ducing the robot's energy consumption through active or passive control strategi es."
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    Tiangong University Reports Findings in Machine Learning (Intelligent alert syst em for predicting invasive mechanical ventilation needs via noninvasive paramete rs: employing an integrated machine learning method with integration of multicen ter ...)

    76-76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 Tianjin, People's Republ ic of China, by NewsRx journalists, research stated, "The use of invasive mechan ical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunct ion. Accurately predicting the demand for IMV is vital for clinical decision-mak ing." The news correspondents obtained a quote from the research from Tiangong Univers ity, "However, current techniques are invasive and challenging to implement in p re-hospital and emergency rescue settings. To address this issue, a real-time pr ediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achiev ing an AUC value of 0.935 (95 % CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpass ing the performance of traditional risk adjustment algorithms (OSI(oxygenation s aturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the mode l's decision-making. These findings highlight the substantial potential of a mac hine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand."
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    Tsinghua University Reports Findings in Robotics (A robot-assisted tracheal intu bation system based on a soft actuator?)

    76-77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 Robotics is the subjec t of a report. According to news reporting from Beijing, People's Republic of Ch ina, by NewsRx journalists, research stated, "Tracheal intubation is the gold st andard of airway protection and constitutes a pivotal life-saving technique freq uently employed in emergency medical interventions. Hence, in this paper, a syst em is designed to execute tracheal intubation tasks automatically, offering a sa fer and more efficient solution, thereby alleviating the burden on physicians." The news correspondents obtained a quote from the research from Tsinghua Univers ity, "The system comprises a tracheal tube with a bendable front end, a drive sy stem, and a tip endoscope. The soft actuator provides two degrees of freedom for precise orientation. It is fabricated with varying-hardness silicone and reinfo rced with fibers and spiral steel wire for flexibility and safety. The hydraulic actuation system and tube feeding mechanism enable controlled bending and deliv ery. Object detection of key anatomical features guides the robotic arm and soft actuator. The control strategy involves visual servo control for coordinated ro botic arm and soft actuator movements, ensuring accurate and safe tracheal intub ation. The kinematics of the soft actuator were established using a constant cur vature model, allowing simulation of its workspace. Through experiments, the act uator is capable of 90° bending as well as 20° deflection on the left and right sides. The maximum insertion force of the tube is 2 N. Autonomous tracheal intub ation experiments on a training manikin were successful in all 10 trials, with a n average insertion time of 45.6 s. Experimental validation on the manikin demon strated that the robot tracheal intubation system based on a soft actuator was a ble to perform safe, stable, and automated tracheal intubation."
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    Recent Studies from University of Nottingham Add New Data to Computational Intel ligence (Low-contrast Medical Image Segmentation Via Transformer and Boundary Pe rception)

    77-78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 originating from Ning bo, People's Republic of China, by NewsRx correspondents, research stated, "Low- contrast medical image segmentation is a challenging task that requires full use of local details and global context. However, existing convolutional neural net works (CNNs) cannot fully exploit global information due to limited receptive fi elds and local weight sharing." Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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