Errors In Interpretation Research Articles

Artifacts Deceive: The Electroretinogram in the Electroencephalogram of a teenager with cerebral anoxia.

Artifacts on an electroencephalogram (EEG) - whether physiologic or non-physiologic - can be common and are important to recognize to prevent errors in interpretation. One such EEG artifact is an electroretinogram (ERG) artifact which occurs during photic stimulation. Typically, of a low amplitude, its presence is usually obscured by normal EEG activity over the frontopolar channels but it can appear prominent in very suppressed or EEG recordings showing electrocerebral inactivity. Overall, rarely reported in the literature, the purpose of this case report is to highlight the presence of an ERG artifact in a teenage boy where EEG was obtained after a cerebral anoxic event. It is important that EEG readers identify this to be a non-cerebral waveform in order to provide an accurate assessment of neurologic prognosis.

Applications and potential of machine learning augmented chest X-ray interpretation in cardiology.

The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have remained constant for decades. The application of machine learning has the potential to substantially improve clinical workflow efficiency, pathology detection accuracy, error rates and clinical decision making in cardiology. To date, machine learning has been developed to improve image processing, facilitate pathology detection, optimize the clinical workflow, and facilitate risk stratification. This review explores the current and potential future applications of machine learning for chest radiography to facilitate clinical decision making in cardiology. It maps the current state of the science and considers additional potential use cases from the perspective of clinicians and technologists actively engaged in the development and deployment of deep learning driven clinical decision support systems.

Australian Dentists' Knowledge of the Consequences of Interpretive Errors in Dental Radiographs and Potential Mitigation Measures.

Dental radiographs, typically taken and interpreted by dentists, are essential for diagnosis and effective treatment planning. Interpretive errors in dental radiographs, stemming from failures of visual and cognitive processes, can affect both patients and clinicians. This survey aimed to assess the dental practitioners' perceptions of the consequences of these errors and potential measures to minimize them. This online anonymized survey assessed Australian dental practitioners' perceptions of the consequences of these errors and potential mitigation measures using ranking, Likert scale, and open-ended questions. The data were analyzed using descriptive statistics and bivariate analysis. Participants identified undertreatment (72%) and legal implications (82%) as the most significant consequences of interpretive errors, whereas severe harm to patients was deemed the least likely. Dental practitioners placed a greater emphasis on maintaining a high level of competence and the well-being of their patients. Utilizing high-quality images (63.9%) and appropriate radiographs (59.7%) were identified as the most effective measures to minimize interpretive errors. Participants showed hesitancy regarding the reliance on machine learning as a clinical decision-making tool. The survey provides valuable practical insights into the consequences and targeted measures to minimize the occurrence of interpretive errors. Efforts to minimize interpretive errors should address patient safety and practitioners' concerns about professional reputation and business viability. The study also suggests further research into the role of machine learning algorithms in reducing interpretive errors in dentistry.

The Time of the Stones: A Call for Palimpsest Dissection to Explore Lithic Record Formation Processes

The dissection of archaeological palimpsests has become a crucial process for achieving a diachronic understanding of the history of human groups. However, its widespread application to archaeological deposits has been hampered by both methodological and theoretical limitations, as well as by the inherent characteristics of the deposits. This paper explores whether overcoming these barriers, both methodological and theoretical, truly represents a significant shift in understanding past human behaviour, thereby motivating the pursuit of shorter timescales. To this end, we have analysed the lithic assemblages of Unit Xb from the Neanderthal site of El Salt (Alcoi, Iberian Peninsula) focusing on lithic attributes and raw material analyses, enabling the definition of raw material units and refitting sets. Considering these variables, we have applied archaeostratigraphic and spatial analyses in order to generate units of analysis whose content is compared to that of the entire unit. The defined archaeostratigraphic units display different spatial distributions and lithic composition. Some of them are attached to certain hearths and composed of refitted sets, while other units are related to areas without combustion evidence and integrated with bigger and heavier single products. Through this approach, here, we show that reducing the spatiotemporal scale of the record helps to unravel behavioural variability, reducing interpretative errors implicit in the assemblage-as-a-whole approach. This highlights the role of temporal resolution in reconstructing site formation processes and challenges research perspectives that assert the unnecessary or impossible nature of palimpsest dissection.

Radiomic Analysis of Cohort-Specific Diagnostic Errors in Reading Dense Mammograms Using Artificial Intelligence.

This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach. Thirty-six radiologists from China and Australia read 60 dense mammograms. For each cohort, we identified normal areas that looked suspicious of cancer and the malignant areas containing cancers. Then radiomic features were extracted from these identified areas and random forest models were trained to recognize the areas that were most frequently linked to diagnostic errors within each cohort. The performance of the model and discriminatory power of significant radiomic features were assessed. We found that in the Chinese cohort, the AUC values for predicting false positives were 0.864 (CC) and 0.829 (MLO), while in the Australian cohort, they were 0.652 (CC) and 0.747 (MLO). For false negatives, the AUC values in the Chinese cohort were 0.677 (CC) and 0.673 (MLO), and in the Australian cohort, they were 0.600 (CC) and 0.505 (MLO). In both cohorts, regions with higher Gabor and maximum response filter outputs were more prone to false positives, while areas with significant intensity changes and coarse textures were more likely to yield false negatives. This cohort-based pipeline proves effective in identifying common errors for specific reader cohorts based on image-derived radiomic features. This study demonstrates that radiomics-based AI can effectively identify and predict radiologists' interpretation errors in dense mammograms, with distinct radiomic features linked to false positives and false negatives in Chinese and Australian cohorts.

Visualizing Convergent Pressures on Arctic Development

A rapidly changing Arctic has impacted biophysical and human systems while creating new economic opportunities. Spatially identifying locations with development potential in this changing environment requires characterizing convergences in critical enabling/constraining factors occurring in a particular place. However, mapping techniques based on simple overlays of spatially heterogeneous data may result in visual clutter, compromising legibility, and increasing the likelihood of interpretation errors. To overcome these limitations, we introduce Pythia, a tool that combines geographic statistical analysis with a subtractive color model to enable bi- or tri-variate data analysis. Three case studies showcase this visualization tool. Case study 1 identifies locations where temperature and population are projected to increase by 2040. Case study 2 reveals locations with a significant presence of major roads and high NO2 concentrations but few hospitals and clinics. In case study 3, a combination of transportation infrastructure, protected areas, and travel and tourism infrastructure signals challenges for the future Alaskan tourism industry. Comparing these results allows for further geographic characterization of locations, aiding policymakers in identifying areas lacking resources and infrastructure, exploring possible futures, and supporting long-term strategic planning.

Incorrect interpretation of the law by the court

The subject of this study is the main trends that have developed in judicial practice related to the assessment of the correctness of the interpretation of the norms of law by the court. The purpose of the study was to identify the main approaches used by law enforcement agencies, primarily courts, in assessing the possible existence of an error in the interpretation of the applied law by a lower court.General scientific and special methods of scientific cognition were used in the research. Among them are analysis, synthesis, deduction, induction, the systematic method, the hermeneutical method and the legal-dogmatic approach. In particular, published court decisions were analyzed, scientific literature and current legislation were studied, after which conclusions were drawn about the state of law enforcement practice and the prevailing trends when considering by higher courts the issue of a possible error in the interpretation of the law committed by a lower court.The main content of the article is the results of the analysis of judicial practice, the purpose of which was to identify the main trends in judicial practice related to the assessment of the correctness of the interpretation of the norms of law by the court. The practice of applying the provisions of the procedural codes of the Russian Federation, providing for the consequences of a misinterpretation of the provisions of normative acts by the court, is considered. As a result of the conducted research, the main problems that courts face when assessing the correctness of the interpretation given by their colleagues from the lower court were identified. During the analysis, special attention was paid to such aspects as the causes of erroneous interpretation, ways to detect and eliminate the identified error, and the consequences of making an error.Based on the results obtained, conclusions were formulated, among which it is worth noting that judicial errors in the interpretation of normative acts are quite common. At the same time, procedural legislation regulates such situations in different ways in different types of legal proceedings. Having discovered a judicial error in interpretation, the higher court most often does not indicate specific norms that have been misinterpreted, but offers its own solution to the dispute. The errors of the courts in the qualification of those mistakes made by the lower courts were also noted.

DetSegDiff: A joint periodontal landmark detection and segmentation in intraoral ultrasound using edge-enhanced diffusion-based network

Individuals with malocclusion require an orthodontic diagnosis and treatment plan based on the severity of their condition. Assessing and monitoring changes in periodontal structures before, during, and after orthodontic procedures is crucial, and intraoral ultrasound (US) imaging has been shown a promising diagnostic tool in imaging periodontium. However, accurately delineating and analyzing periodontal structures in US videos is a challenging task for clinicians, as it is time-consuming and subject to interpretation errors. This paper introduces DetSegDiff, an edge-enhanced diffusion-based network developed to simultaneously detect the cementoenamel junction (CEJ) and segment alveolar bone structure in intraoral US videos. An edge feature encoder is designed to enhance edge and texture information for precise delineation of periodontal structures. Additionally, we employed the spatial squeeze-attention module (SSAM) to extract more representative features to perform both detection and segmentation tasks at global and local levels. This study used 169 videos from 17 orthodontic patients for training purposes and was subsequently tested on 41 videos from 4 additional patients. The proposed method achieved a mean distance difference of 0.17 ± 0.19 mm for the CEJ and an average Dice score of 90.1% for alveolar bone structure. As there is a lack of multi-task benchmark networks, thorough experiments were undertaken to assess and benchmark the proposed method against state-of-the-art (SOTA) detection and segmentation individual networks. The experimental results demonstrated that DetSegDiff outperformed SOTA approaches, confirming the feasibility of using automated diagnostic systems for orthodontists.

P > 0.05 is Good: The NORD-h Protocol for Several Hypothesis Analysis Based on Known Risks, Costs, and Benefits.

Statistical testing in medicine is a controversial and commonly misunderstood topic. Despite decades of efforts by renowned associations and international experts, fallacies such as nullism, the magnitude fallacy, and dichotomania are still widespread within clinical and epidemiological research. This can lead to serious health errors (e.g., misidentification of adverse reactions). In this regard, our work sheds light on another common interpretive and cognitive error: the fallacy of high significance, understood as the mistaken tendency to prioritize findings that lead to low p-values. Indeed, there are target hypotheses (e.g., a hazard ratio of 0.10) for which a high p-value is an optimal and desirable outcome. Accordingly, we propose a novel method that goes beyond mere null hypothesis testing by assessing the statistical surprise of the experimental result compared to the prediction of several target assumptions. Additionally, we formalize the concept of interval hypotheses based on prior information about costs, risks, and benefits for the stakeholders (NORD-h protocol). The incompatibility graph (or surprisal graph) is adopted in this context. Finally, we discuss the epistemic necessity for a descriptive, (quasi) unconditional approach in statistics, which is essential to draw valid conclusions about the consistency of data with all relevant possibilities, including study limitations. Given these considerations, this new protocol has the potential to significantly impact the production of reliable evidence in public health.

Intercept evidence from foreign language communications: Reliability and minimum standards in the interests of justice.

This paper explores the role of Intercept Interpreters/Translators (IITs) in law enforcement communication surveillance efforts. It focuses on the production and reliability of Translated Intercept Records (TIR), which are comprehensive written records in the target language that may be produced for intelligence purposes or for use in court as Translated Intercept Evidence (TIE). The paper underscores the critical importance of reliable TIR for both evidentiary use and operational decision-making. The authors emphasise the need to establish minimal standards in the quest for reliability and dispel the misconception that literal translations fulfil evidentiary requirements. The standards proposed in this paper aim to minimise interpretation errors to enhance the overall effectiveness of investigations and safeguard the interests of justice. The paper concludes by highlighting the need to align judicial expectations with sound translation practices.

The Azygos Esophageal Recess Is Not to Be Missed in Screening Lung Cancer With LDCT.

Lesion overlooking and late diagnostic workup can compromise the efficacy of low-dose CT (LDCT) screening of lung cancer (LC), implying more advanced and less curable disease stages. We hypothesized that the azygos esophageal recess (AER) of the right lower lobe (RLL) might be an area prone to lesion overlooking in LC screening. Two radiologists reviewed the LDCT examinations of all the screen-detected incident LCs observed in the active arm of 2 randomized clinical trials: ITALUNG and national lung screening trial. Those in the AER were compared with those in the remainder of the RLL for possible differences in diagnostic lag according to the Lung-RADS 1.1 recommendations, size, stage, and mortality. Six (11.7%) of 51 screen-detected incident LCs of the RLL were located in the AER. The diagnostic lag time was significantly longer (P=0.046) in the AER LC (mean 14±9mo) than in the LC in the remaining RLL (mean 7.3±1mo). Size and stage at diagnosis were not significantly different. All 6 subjects with LC in the AER and 16 (35.5%) of 45 subjects with LC in the remaining RLL (P=0.004) died of LC after a median follow-up of 12 years. Our retrospective study indicates that AER might represent a lung region of the RLL prone to have early LC overlooked due to detection or interpretation errors with possible detrimental consequences for the subject undergoing LC screening.

Accuracy of Reaction Time Measurement on Automated Neuropsychological Assessment Metric UltraMobile.

This observational study examined the accuracy of simple reaction time (RT) measurements on various touchscreen tablet devices using the Automated Neuropsychological Assessment Metric (ANAM) UltraMobile test battery. The study investigated the implications of interpreting ANAM UltraMobile with laptop-based normative data by analyzing the magnitude and variability of RT accuracy across devices. RT accuracy on 10 different tablets was assessed using a photodetector and robotic arm to respond to stimuli at predetermined response times. The recorded RT was compared with the true RT obtained from the robotic arm to calculate the RT error. ANAM UltraMobile recorded slower RTs than the laptop version. Additionally, RT error varied considerably among the 10 tablet models, suggesting psychometrically significant implications that could lead to interpretive errors when using laptop-based normative data. Relative to the RT error from the laptop-based version of ANAM, tablet data from ANAM UltraMobile are significantly slower and exhibit large variability between devices. These differences may have clinically significant implications for the comparability of the two versions. The findings suggest that further research with human participants is needed to assess the equivalence of ANAM UltraMobile with its predecessor.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

How to improve medical students’ ECG interpretation skills ? Multicenter survey and results of a comparative study evaluating a new educational approach

BackgroundLearning to interpret electrocardiograms (ECGs) is a crucial objective in medical education. Despite its importance, errors in ECGs interpretation are common, and the optimal teaching methods have not yet been clearly established.ObjectivesTo evaluate students’ confidence in ECGs analysis and their opinion on current teaching methods, and to assess the effectiveness of a new ECG educational approach.MethodsFirst, we conducted a survey on ECG learning among fourth to sixth-year medical students. Second, a 5-week multicenter comparative study was conducted with fourth-year medical students during their cardiology internship. Two different teaching methods were used, assigned by center. The first group participated in 5-minutes workshops 4 times a week using a “reversed classroom” method, supervised by a cardiologist, where students took turns selecting, presenting and discussing ECGs. The control group attended a single 2-hour face-to-face ECG course. All participants completed a 30-minute ECGs analysis test at baseline and after 5 weeks.ResultsOut of 401 survey respondents, the confidence levels in ECG interpretation were 3/5 (IQR 2–3) for routine situations and 2/5 (IQR 1–3) for emergency situations. Satisfaction with ECG teaching was low (2/5, IQR 1–3) and 96.3% of respondents favored more extensive ECG training. In the comparative study, 52 students from 3 medical schools were enrolled (control group: n = 27; workshop group: n = 25). Both groups showed significant improvement in exam scores from baseline to 5-week (33/100 ± 12/100 to 44/100 ± 12/100, p < 0.0001 for the control group and 36/100 ± 13/100 to 62/100 ± 12/100, p < 0.0001 for the workshop group). The improvement was significantly greater in the workshop group compared to the control group (+ 26 ± 11 vs. + 11 ± 6, p < 0.001).ConclusionsAmong French medical students who initially reported low confidence and insufficient skills in ECG interpretation, the workshop approach using a “reversed classroom” method was found to be more effective than conventional lecture-based teaching during cardiology internship.Graphical abstract

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

Analisis Kesalahan Mahasiswa Dalam Menyelesaikan Soal Pembuktian Teorema Binomial Pada Sifat Simetrik

This research was conducted to observe students' error in working on problems proving the binomial theorem on the symmetric properties of binomial coefficients. This type of research is qualitative research. This research uses a test method, namely by working on questions followed by interviews. This research involved students from the Tadris Mathematics Study Program at Al Falah As Sunniyah Kencong Jember University in the second semester of the 2023/2024 academic year with a total of 17 students. The 17 students were grouped into 3 cognitive abilities, namely high, medium and low cognitive. The subjects of this research consisted of three students with each student representing each cognitive ability group. The results of the research show that the types of language interpretation errors are made by subjects with high and moderate cognitive abilities, namely not writing the correct sentences when drawing conclusions. Conceptual errors were made by subjects with medium and low cognitive abilities in the form of errors in the proof steps. Procedural errors were made by subjects with moderate and low cognitive abilities, namely not having carried out complete calculations when applying the theorem in numerical form. Technical errors were made by subjects with low cognitive abilities, namely writing the same steps repeatedly. Errors in drawing conclusions were made by subjects with high, medium and low cognitive abilities. The three subjects considered the verification process to be complete. All errors are caused by a lack of understanding of the concept, carelessness in the proof process and not being careful in reading the questions.

Methodological Planning and Data Collection. Ensuring a Valid and Credible Research of a Questionnaire Survey in Social Work

The validity and credibility of scientific research are determined by the strictness of planning and elaborating each stage of the research, by correlating the purpose of the research with the chosen research method, by avoiding methodological planning errors, as well as by carrying out each research stage in a manner that excludes errors in data collection, processing and interpretation. In the field of social work, par excellence, the value of an empirical research is conferred, among other things, by the possibility of extrapolating the conclusions of that research to similar situations in the practical activity of social workers, in case work with the beneficiaries of social work services. Due to the close connection between research and practice in this field of social sciences, the methodological design must be transparent and convincing, so as to give practitioners a high degree of confidence in the conclusions of the scientific study, as well as the possibility to capitalize on its informative benefits. So, methodological errors of research projects carried out in the field of social work can have a much wider and more tangible negative impact than in the case of other fields of science, at least from a pragmatic point of view, when theory is translated to practical applicability, and the scientific study is implemented in case work performed with the beneficiaries. This study aims to analyze the questionnaire survey in social work, by highlighting the main methodological errors that can affect the validity of such an empirical study, in order to identify the optimal solutions for a correct planning and methodological development of the scientific study.

Understanding the Reaction Processes in Li-Ion Batteries By Voltage Relaxation Analysis

The open circuit voltage (OCV) transient observed after current interruption is a very useful electrochemical signal providing crucial information about a Li-ion battery (LIB), including its state of charge and health, and kinetics of internal processes. Generally, OCV transient is divided into immediate and long-term relaxations, which are probably related to current-dependent kinetic processes and changes in the instantaneous thermodynamic state, respectively. Previous studies have attempted to analyze the pulse and relaxation voltages of LIBs under Li concentration gradients in both the electrolyte and electrode, with much success. Nevertheless, they have been limited in understanding what is really happening inside the cell in detail. This was mainly due to the fact that the experiments were performed on full cells with overlapping anode and cathode signals, which hindered the interpretation of detailed internal reactions, and for the same reason, only unsystematic phenomenological interpretations were possible. So it is necessary to separate relaxation signals from the anode and cathode for more accurate analysis. Even in a half-cell, where the signal contribution of the Li counter electrode (CE) to the overall cell signal is believed to be very small, the degree of potential relaxation attributable to the CE might be non-negligible due to the concentration change of active ions around the electrode. Therefore, in order to fully understand the electrochemical characteristics of the working electrode (WE), a precise analysis of the voltage relaxation of a single electrode is required.In this study, various test cells, including three-electrode half-cells and full cell, and a voltage-controllable symmetrical cell, were fabricated to analyze the pulse and relaxation process in order to further understand the reaction kinetics of the battery. In particular, we aimed to distinguish the detailed processes and to understand the overvoltage evolution during battery operation. For this purpose, Ni-rich layered oxide and graphite were used as the cathode and anode, respectively. Li metal was adopted as the CE and RE in both the half-cell and three-electrode cells. In the two- and three-electrode cathode half-cell experiments, it was confirmed that the overpotential contribution of Li CE in half-cells was one-third to half of total overpotential in specific operating conditions. Considering the relatively fast redox reaction rate of Li, this result strongly implies that a concentration overpotential due to the CE can play a critical role in OCV relaxation. In other words, a typical test cell structure where the opposing areas of WE and CE are similar, total cell overpotential is greatly affected by CE. Supporting this argument, a cell designed with a large CE area and abundant electrolyte showed the very small contribution of CE. In experiments with two- and three-electrode anode half-cells, the contribution of Li CE was similar to that of the cathode half-cell. Next, to investigate the change in the effect of Li CE on the overall overpotential as the cell degrades under repeated cycles, the OCV relaxation with cycles was analyzed for the test cells used above. The analysis revealed an obvious cycle dependence of the long-term OCV relaxation signal, which was confirmed to be largely an effect of Li CE. To investigate the unexpected voltage behavior of Li, Li symmetric cells were configured to obtain voltage curves over time with cycling. From the analysis results, it is proposed that the voltage curve variation is mainly due to different local reaction resistances and highly tortuous surface morphology of the Li surface.In conclusion, the overpotential of Li CE had a large impact on total overpotential in the commonly used two-electrode half-cells, which may cause errors in voltage curve interpretation. Under these circumstances, we further investigated whether the systematic and thorough analysis of the OCV relaxation could reliably separate the cathode and anode overpotential signals in the full cell, as well as the detailed reaction signals at each electrode. To this end, we performed an overpotential analysis using two- and three-electrode cells, as well as a voltage-controllable symmetrical cell, cross-validating the results with those obtained in the half-cell experiments above and electrochemical impedance spectroscopy. In this presentation, we will show how to derive the overpotential contribution of the cathode and anode, and the detailed reaction processes from the short- and long-term OCV relaxation curves obtained from a full-cell. Furthermore, we will discuss the reliability of the separated signals and possible future work to improve their accuracy.

Measurement of Cardiothoracic Ratio on Chest X-rays Using Artificial Intelligence-A Systematic Review and Meta-Analysis.

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and meta-analysis evaluate the efficacy of artificial intelligence (AI) in automating CTR determination to enhance patient care and streamline diagnostic processes. They are concentrated on comparing the performance of AI models in determining the CTR against human assessments, identifying the most effective models for potential clinical implementation. This study was registered with PROSPERO (no. CRD42023437459). No funding was received. Methods: A comprehensive search of medical databases was conducted in June 2023. The search strategy adhered to the PICO framework. Inclusion criteria encompassed original articles from the last decade focusing on AI-assisted CTR assessment from standing-position CXRs. Exclusion criteria included systematic reviews, meta-analyses, conference abstracts, paediatric studies, non-original articles, and studies using imaging techniques other than X-rays. After initial screening, 117 articles were reviewed, with 14 studies meeting the final inclusion criteria. Data extraction was performed by three independent investigators, and quality assessment followed PRISMA 2020 guidelines, using tools such as the JBI Checklist, AMSTAR 2, and CASP Diagnostic Study Checklist. Risk of bias was assessed according to the Cochrane Handbook guidelines. Results: Fourteen studies, comprising a total of 70,472 CXR images, met the inclusion criteria. Various AI models were evaluated, with differences in dataset characteristics and AI technology used. Common preprocessing techniques included resizing and normalization. The pooled AUC for cardiomegaly detection was 0.959 (95% CI 0.944-0.975). The pooled standardized mean difference for CTR measurement was 0.0353 (95% CI 0.147-0.0760). Significant heterogeneity was found between studies (I2 89.97%, p < 0.0001), with no publication bias detected. Conclusions: Standardizing methodologies is crucial to avoid interpretational errors and advance AI in medical imaging diagnostics. Uniform reporting standards are essential for the further development of AI in CTR measurement and broader medical imaging applications.

Unusual Multiple and Bilateral Combinations of Arterial and Neural Variations in the Axillae of Single Cadaver

Nerve During the dissection of the axillae and upper limbs of an 80-year-old male donor, multiple vascular and neural variations were encountered. On the right side, the thoracoacromial artery and the lateral thoracic artery were absent. On the left side the subscapular artery was absent, and the posterior cord of the brachial plexus gave branch to two upper subscapular nerves and then divided into radial nerve and a common stem that branched into thoracodorsal nerve, lower subscapular nerve, nerves to teres major and minor muscles and the axillary nerve. Additionally, in the left arm there was a variant communication between the musculocutaneous nerve and the median nerve. Although the existence of these variations is well documented in the literature as individual entities or few combinations, the occurrence of such multiple variations in a single cadaver in a limited area of the body involving vessel and nerves is not common. A multitude of variations in a small area of the body such as this can be a source of diagnostic and imaging interpretation errors. Unpredictable iatrogenic injuries can also occur during various procedures in the area such as during preparation of muscle flap, mastectomy with axillary lymph node dissection, reconstructive and aesthetic plastic surgery of the breast and other clinical procedures including shoulder arthroplasty.