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    University Hospital y Politecnico la Fe Reports Findings in Bioethics ([Translated article] Bioethical Conflicts in Current Dermatolo gy: A Narrative Review)

    20-21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 Bioengineering - Bioet hics is the subject of a report. According to news originating from Valencia, Sp ain, by NewsRx correspondents, research stated, “Both the functions and equipmen t of dermatologists have increased over the past few years, some examples being cosmetic dermatology, artificial intelligence, tele-dermatology, and social medi a, which added to the pharmaceutical industry and cosmetic selling has become a source of bioethical conflicts. The objective of this narrative review is to ide ntify the bioethical conflicts of everyday dermatology practice and highlight th e proposed solutions.” Our news journalists obtained a quote from the research from University Hospital y Politecnico la Fe, “Therefore, we conducted searches across PubMed, Web of Sc ience and Scopus databases. Also, the main Spanish and American deontological co des of physicians and dermatologists have been revised. The authors recommend de claring all conflicts of interest while respecting the patients’ autonomy, confi dentiality, and privacy. Cosmetic dermatology, cosmetic selling, artificial inte lligence, tele-dermatology, and social media are feasible as long as the same st andards of conventional dermatology are applied.”
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    Reports on Robotics from University of Manchester Provide New Insights (Effects of the Human Presence Among Robots In the Ariac 2023 Industrial Automation Compe tition)

    21-22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 from Manchester, United Kingdom, by NewsRx j ournalists, research stated, “ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary i ndustry problems to be solved using agile robotics. For the 2023 competition, AR IAC competitors must perform assembly and kitting tasks by controlling four auto nomous ground vehicles (AGVs), one floorbased robot, and one ceiling-based (Gan try) robot in an attempt to overcome a range of agility challenges in the suppli ed simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo.” Financial support for this research came from Royal Academy of Engineering - UK.
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    University Hospital San Juan de Alicante Reports Findings in Machine Learning (A step forward in the diagnosis of urinary tract infections: from machine learnin g to clinical practice)

    22-23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 from Alicante, Sp ain, by NewsRx correspondents, research stated, “Urinary tract infections (UTIs) are common infections within the Emergency Department (ED), causing increased l aboratory workloads and unnecessary antibiotics prescriptions. The aim of this s tudy was to improve UTI diagnostics in clinical practice by application of machi ne learning (ML) models for real-time UTI prediction.” Our news editors obtained a quote from the research from University Hospital San Juan de Alicante, “In a retrospective study, patient information and outcomes f rom Emergency Department patients, with positive and negative culture results, w ere used to design models - ‘Random Forest’ and ‘Neural Network’ - for the predi ction of UTIs. The performance of these predictive models was validated in a cro ss-sectional study. In a quasi-experimental study, the impact of UTI risk assess ment was investigated by evaluating changes in the behaviour of clinicians, meas uring changes in antibiotic prescriptions and urine culture requests. First, we trained and tested two different predictive models with 8692 cases. Second, we i nvestigated the performance of the predictive models in clinical practice with 9 62 cases (Area under the curve was between 0.81 to 0.88). The best performance w as the combination of both models. Finally, the assessment of the risk for UTIs was implemented into clinical practice and allowed for the reduction of unnecess ary urine cultures and antibiotic prescriptions for patients with a low risk of UTI, as well as targeted diagnostics and treatment for patients with a high risk of UTI.”
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    Data from Laureate Institute for Brain Research Provide New Insights into Machin e Learning (Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women)

    23-24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 Tulsa, Oklahoma, by NewsRx j ournalists, research stated, “IntroductionIn the US, women are one of the fastes t-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversio n programs can be effective.” The news correspondents obtained a quote from the research from Laureate Institu te for Brain Research: “It may be beneficial to identify individuals who are mos t likely to complete the program versus terminate early as this can provide info rmation regarding who may need additional or unique programming to improve the l ikelihood of successful program completion. Prior research investigating predict ion of success in these programs has primarily focused on demographic factors in male samples. MethodsThe current study used machine learning (ML) to examine ot her non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, sel f-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, us ing repeated, nested cross-validation, predicting subsequent graduation versus t ermination from the program. An identical ML analysis was conducted for model tw o, in which 34 variables were entered, including the Women’s Risk/Needs Assessme nt (WRNA).ResultsML models were unable to predict graduation at an individual le vel better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses i ndicated measures of impulsivity, trauma history, interoceptive awareness, emplo yment/financial risk, housing safety, antisocial friends, anger/hostility, and W RNA total score and risk scores exhibited medium to large effect sizes in predic ting treatment completion (p <0.05; ds = 0.29 to 0.81).”
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    Ningbo No. 2 Hospital Researcher Discusses Research in Machine Learning (Enhanci ng the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging)

    24-24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 reporting out of Zhejiang, People’s Rep ublic of China, by NewsRx editors, research stated, “Aims/Background Sacroiliiti s is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies.” Our news reporters obtained a quote from the research from Ningbo No. 2 Hospital : “This study aims to improve the diagnostic accuracy of sacroiliitis by applyin g advanced machine learning techniques to computed tomography (CT) images. We em ployed five convolutional neural network (CNN) models-Visual Geometry Group 16-l ayer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analy ze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. Each model’s performance was evaluated using metrics such as accuracy, pr ecision, recall, F1 score, Receiver Operating Characteristic (ROC), and Area Und er the Curve (AUC). The interpretability of the models’ decisions was enhanced u sing Gradient-weighted Class Activation Mapping (Grad-CAM) visualization. The Re sNeXt-50 and Inception-v4 models demonstrated superior performance, achieving th e highest accuracy and F1 scores among the tested models. Grad-CAM visualization s offered insights into the decision-making processes, highlighting the models’ focus on relevant anatomical features critical for accurate diagnosis.”
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    Nanjing University of Aeronautics and Astronautics Researcher Illuminates Resear ch in Robotics (Robotic Valve Turning with a Wheeled Mobile Manipulator via Hybr id Passive/Active Compliance)

    25-25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 robotics have been pr esented. According to news originating from Nanjing, People’s Republic of China, by NewsRx editors, the research stated, “This paper addresses the problems of v alve-turning operation in rescue environments where a wheeled mobile manipulator (WMM) is employed, including the possible occurrence of large internal forces.” Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Jiangsu Province; State Key Laborator y of Robotics And Systems; National Key Research And Development Program of Chin a; “111” Project.
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    Henan University Reports Findings in Machine Learning (Using machine learning mo dels to identify the risk of depression in middleaged and older adults with fre quent and infrequent nicotine use: A cross-sectional study)

    26-26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 Kaifeng, People’s Repu blic of China, by NewsRx editors, research stated, “Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of d epression among middle-aged and older adults with frequent and infrequent nicoti ne use, as this is quite necessary for supporting their well-being.” Our news journalists obtained a quote from the research from Henan University, “ This study included a total of 10,821 participants, which were derived from the China Health and Retirement Longitudinal Study Wave 5, 2020 (CHARLS-5). Five mac hine learning (ML) algorithms were employed. Some metrics were used to evaluate the performance of models, including area under the receiver operating character istic curve (AUC), positive predictive value (PPV), specificity, accuracy. 10,82 1 participants (6472 males, 4349 females) had a mean age of 60.47 ± 8.98, with a score of 8.90 ± 6.53 on depression scale. For middle-aged and older adults with frequent nicotine use, random forest (RF) achieved the highest AUC value, PPV a nd specificity (0.75, 0.74 and 0.88, respectively). For the other group, support vector machines (SVM) showed the highest PPV (0.74), and relatively high accura cy and specificity (0.72 and 0.87, respectively). Feature importance analysis in dicated that ‘dissatisfaction with life’ was the most important variable of iden tifying the risk of depression in the SVM model, while ‘attitude towards expecte d life span’ was the most important one in the RF model. CHARLS-5 was collected during the COVID-19, so our results may be influenced by the pandemic.”
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    Data from University of Queensland Provide New Insights into Machine Learning (O ptimization of a Coal Mine Roof Characterization Model Using Machine Learning)

    27-28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 originating in St. Lucia, Australia , by NewsRx journalists, research stated, “Predicting areas of increased propens ity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing roc k mass strength or characterization indices in isolation.” Financial support for this research came from Department of Industry, Science an d Resources and Geotechnicoal Pty Ltd..
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    Data on Robotics Reported by Andrzej Michnik and Colleagues (Multivariate analys is of the kinematics of an upper limb rehabilitation robot)

    28-28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 out of Zabrze, Poland, by NewsRx edit ors, research stated, “The purpose of this work is to present a multivariate ana lysis of the kinematics of an upper limb rehabilitation robot. Comparing multipl e concepts of kinematic chains makes it possible to identify advantages and disa dvantages and, as a consequence, choosing the optimal solution to create a physi cal device.” Our news journalists obtained a quote from the research, “Such actions shall con tribute towards automation of the rehabilitation process, bringing benefits to b oth therapists and patients in comparison with conventional rehabilitation. Mult ivariate analysis of kinematics was performed on the basis of three concepts of the kinematic chain of an exoskeleton, enabling the rehabilitation of both right and left upper limb within the area of the shoulder joint, elbow joint and wris t. The kinematic chain allows the performance of simple and complex movements. T he results of the conducted multivariate kinematic analysis define specific move ments and angular ranges, which may be performed while applying one of the propo sed concepts of the robot design. The results made it possible to determine the optimum solution to the kinematic diagram and construction design, which best sa tisfy the expectations for effective rehabilitation. The analysis of the kinemat ic diagram concept of the exoskeleton should be done in relation to its design ( construction form). Considering the obtained parameters, it is necessary to find an optimum concept and wisely manoeuvre the values, in order to avoid a situati on in which one significant parameter influences another, equally important one. ”
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    University Hospital Reports Findings in Endometrial Cancer (Comparing oncologica l outcomes of robotic versus open surgery in the treatment of endometrial cancer )

    29-29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 - Endometrial Cancer is the subject of a report. According to news reporting out of Poole, Un ited Kingdom, by NewsRx editors, research stated, “Robotic surgery has been inco rporated in the treatment of endometrial cancer, with evidence suggesting that m inimal access surgery offers advantages over laparotomy including less blood los s, lower rate of perioperative complications, and accelerated postoperative reco very. The laparoscopic approach to cervical cancer (LACC) study has recently dem onstrated inferior survival outcomes in cervical cancer patients treated with mi nimal access surgery including robotic surgery.” Our news journalists obtained a quote from the research from University Hospital , “It is, therefore, imperative that further evaluation of the latter in endomet rial cancer is performed. A retrospective analysis of clinical data was performe d. We compared two different types of surgery performed for the treatment of FIG O stage 1 to 3 endometrial cancer; open surgery performed in the years 2013-2015 vs robotic surgery performed in 2017-2019, after the implementation of the robo tic program in our institution. Main outcome measures were recurrence-free survi val and overall survival, with secondary outcomes including surgical morbidity a nd postoperative recovery. We compared 123 patients who had open surgery with 10 4 patients who underwent robotic surgery. One case from the second group was con verted to open surgery due to the inability to complete it robotically. After a median follow-up of 68 months, there was no difference in recurrence-free surviv al or overall survival between the two groups. Length of stay after an operation was significantly different with mean hospital stay of 1.6 days after robotic s urgery and 5 days after open surgery (p = 0.001). No significant difference was identified in the rate of complications (p = 0.304).”
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