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ObjectivesTo explore the prevalence and discrepancy in the diagnosis of severe AS (aortic stenosis) by echocardiography in patients referred for TAVI-CT (Transcatheter Aortic valve Implantation CT) and accuracy of CT-AVCS (CT-Aortic valve calcium score) and CT-AVA (CT-aortic valve area) compared to echocardiographic mean gradient (MG) in patients with normal function and flow.Methods1150 patients from three centres who underwent TAVI-CT systolic phase ECG-gating were identified. Patients with previous aortic valve surgery, aortic, or mitral regurgitation were excluded. Patients with left ventricular ejection fraction ≥ 50% and normal indexed stroke volume (28–48 ml/m2) were selected. CT-AVA was obtained by direct planimetry. Accuracy of echo and CT parameters for predicting stenosis severity against MG ≥40 mmHg was determined.ResultsA total of 428 patients (54% women) with a mean age of 81 years (±7.5) fulfilled the inclusion criteria (Table 1). Severe AS by EVA (Echo valve area) ≤1 cm2, peak velocity ≥4 m2, and MG ≥40 mmHg, was present in 95%, 56%, and 56% patients respectively. Severe AS defined by AVCS ≥3000 in men, AVCS ≥2000 in women, and CT-AVA ≤1 cm2 was present in 53%, 59%, and 59% with AUC of AVCS and CT-AVA being (AUC, 0.72–0.74) and (0.69) respectively.ConclusionMost patients referred for TAVI have severe AS defined by EVA with high discrepancy compared to MG. CT-AVCS and CT-AVA are flow-independent parameters with least discrepancy with MG.

Introduction The potential impact of skin pigmentation on the accuracy of oximetric assessment has long been known to be a clinically relevant matter. During the COVID pandemic it became apparent that oximeters may underestimate oxyhemoglobin saturation in darkly pigmented individuals. Validation data for SaO2 is not generally available for the many consumer wearable oximeter devices. Circul® (Bodimetrics Corp, Los Angeles, CA), a wearable oximeter with a form factor of a ring, measures several variables for cardiorespiratory assessment (SpO2, movement, heart rate). We aimed to evaluate the accuracy this device in the measurement of SaO2 compared to a simultaneous values obtained by measurement of SaO2 by arterial blood gases. Methods 24 subjects (8 Black, 16 Non-Black) participated in the validation of the Circul device. In an operating suite they had catheters inserted into their radial arteries. They were administered nitrogen rich air which made them hypoxic in steps to an SaO2 of 70%. Arterial blood was sampled at various levels of hypoxia and simultaneous readings were obtained from the Circul device. We compared SaO2 obtained by Circul vs measurement of ABGs by a medical grade blood gas analyzer. Results There was excellent correlation between SaO2 measured by the ring oximeter and ABGs in both Black (y = 1.0174x - 1.573; R² = 0.9414) and Non-Black (y = 1.0209x - 2.5607; R² = 0.9207) subjects. No significant differences were found in comparing the intercept and slopes of the regressions. At ABG of 70% and 100% the SaO2 measured by the ring was calculated to be 69.6% and 100.2% for the Black subjects and 68.9% and 99.5% for the non-Black subjects. Conclusion Results from this study confirmed that Circul oximetry accuracy seems to be independent of the skin tone. Support (if any)

Introduction During positive airway pressure (PAP) therapy for sleep apnea syndromes, the machine detected apnea hypopnea index (AHI) is an important method for clinicians to evaluate the beneficial effects of PAP. There are concerns about the accuracy of this detection, which also confounds a related question-how common and severe are residual events on PAP. Our study aimed for estimating the long term accuracy of machine detected AHI and the predictors. Methods Subjects with OSA who underwent a split night polysomnography were recruited prospectively. Those treated with PAP and tracked by the EncoreAnywhereTM system were analyzed. The ones who stopped PAP within one month were excluded for this analysis. Compliance, therapy data and waveform data were analyzed. Machine detected versus manually scored events were compared at the 1st, 3rd, 6th and 12th month from PAP initiation, and logistic regression was done to explore the factors associated with a high AHI difference. Results One hundred and seventy-two patients with mean age of 58.79 ± 13.80 and 63.4% male were included. The differences between the machine detected AHI and manual scored AHI was 10.72 ±8.43 in the first month and were stable for up to 12 months. Male sex, large leak ≥ 1.5% of the whole night, titration arousal index ≥ 15 times/hour, and higher ratio of unstable breathing were associated with AHI difference ≥ 5 times/hour. Conclusion The limited agreement between machine detected AHI in the tracking system and manually scored AHI persists for up to 12 months. Gender, large leak, the amount of unstable breathing on PAP, and arousal index during the titration were factors associated with this inaccuracy. Support (if any) positive airway pressure, apnea hypopnea index; detection accuracy

BackgroundRobust and accurate classification of protein expression on individual cells is a critical step in automated and scalable analysis of multiplex tissue images. Achieving expert-level reliability requires well-trained computational models, but accurate cell type annotation remains a major bottleneck. Optimizing which training patches are labeled offers a practical and efficient route to resolve this. Here, we evaluated whether enforcing visual diversity during patch selection improves model calibration when labeled data are scarce.MethodsTwo sampling strategies were compared on human tonsil sections imaged with the Akoya PhenoCycler-Fusion platform, using CD11c as a reference marker:1) R-signal - signal-weighted random sampling based solely on CD11c intensity, and2) ER-signal - the same intensity weighting applied within 40 clusters generated from DINOv2 embeddings.Patch sets of 500-3500 examples were drawn from two independently imaged sections, and a convolutional neural network was trained five times per condition. Performance on held-out slides was measured with balanced accuracy, macro-F1, and the low-confidence rate (softmax < 0.60).ResultsER-signal lowered low-confidence predictions when data were limited. At 500 training patches, the low-confidence rate was 14% for ER-signal versus 26% for R-signal. At 1000 patches, it was 8% versus 10%. Above 2000 patches the rates converged to ≈ 5%.Overall accuracy was comparable. Both strategies reached similar balanced accuracy (~0.81) and macro-F1 (~0.78) by 2500-3000 patches; differences were minor and variable.ConclusionsEmbedding-guided patch selection improves early model confidence without compromising final accuracy, cutting uncertain calls by roughly half when only a few hundred labels are available. As training sets grow, the benefit diminishes, but the early gains underscore the value of embedding-aware sampling for annotation-efficient, high-plex image analysis. These results highlight the benefits of embedding-guided patch selection in multiplexed spatial proteomics images.

Background: The objective of this study was to assess the quality and accuracy of visual backgrounds published in academic surgical journals. Visual backgrounds are commonly used to disseminate medical research findings. They distill the key messages of a research article, presenting them graphically in an engaging manner so that potential readers can decide whether to read the complete manuscript. Methods: We developed a Visual Background Assessment Tool based upon published guidelines. Seven reviewers underwent iterative training to apply the tool. We collected visual backgrounds published by 25 surgical journals from January 2017 to April 2021; those corresponding to systematic reviews without meta-analysis, conference backgrounds, narrative reviews, video backgrounds, or nonclinical research were excluded. Included visual backgrounds were scored on accuracy (as compared with written backgrounds) and design and were given a first impression score. Results: Across 25 surgical journals, 1325 visual backgrounds were scored. We found accuracy deficits in the reporting of study design (35.8%), appropriate icon use (49%), and sample size reporting (69.2%) as well as design deficits in element alignment (54.8%) and symmetry (36.1%). Overall scores ranged from 9 to 14 (out of 15), accuracy scores ranged from 4 to 8 (out of 8), and design scores ranged from 3 to 7 (out of 7). No predictors of visual background score were identified. Conclusion: Visual backgrounds vary widely in quality. As visual backgrounds become integrated with the traditional components of scientific publication, they must be held to similarly high standards. We propose a checklist to be used by authors and journals to standardize the quality of visual backgrounds.

Medicine Bottle Rulers (MBRs) are validated tools designed to estimate liquid volumes accurately, thus avoiding repeat liquid measuring, leading to losses over time. Over the past 24 months, MBRs have been implemented to facilitate daily liquid-controlled drug (CD) checks. Despite their benefits, the implementation has been met with several issues, such as the availability of brand-specific MBRs (MBRs are only valid for that specific bottle, which would be brand/strength specific), compliance and, in turn, general uptake.Aim This study aimed to evaluate MBR usage and user perception 24 months post-implementation and simultaneously identify barriers to their continued use for daily CD checks.MethodA longitudinal audit was conducted using an online survey at three intervals post implementation: 9-months (n=25), 16-months (n=13), and 24-months (n=27). The survey measured the frequency of MBR usage, speed of CD checks, discrepancies, ease-of-use, and usefulness. Qualitative data was also collected to understand specific issues and user experiences.ResultsFrequency of MRB Use Used every time: Usage dropped from 60% (9Months) to 44.4% (24Months).Sometimes used: Usage increased from 20% (9 months) to 44.4% (24 months). Speed of CD Checks Consistently, participants found MRB use speed-up checks: 76% (9 Months), 76.9% (16 months), and 77.8% (24 months). Reduction of Discrepancies ‘Perceived effectiveness’ in reducing discrepancies decreased from 54.2% (9 Months) to 44% (24 Months).Responses stating, they found no reduction in CD discrepancies increased from 41.7% (9 months) to 56% (24 months). Ease of Use The majority found MRBs easy to use. 92% (9 months) to 81.5% (24 months). Usefulness Rating Mode Averages: 9 Months: ‘Somewhat-useful’ 24%; 16 Months- ‘Somewhat-useful’ 54%; 24 Months ‘Very-useful’ 33%‘Extremely useful’ ratings decreased from 16% (9 Months) to 7.4% (24 Months). Discussion and ConclusionThe results indicate a trend of decreasing consistent usage and divided perceptions of the effectiveness of MBRs. While MBRs speed up CD checks and are easy to use, significant concerns about their accuracy, availability, and visibility persist. These issues hinder their full adoption and effectiveness in reducing discrepancies in liquid CDs. Addressing these concerns through education and distributing MBRs is essential to enhance user satisfaction and overall utility in clinical practice.The study highlights the critical need to address barriers to using MBRs effectively. User feedback suggests that concerns about accuracy (despite them being validated) should be investigated. Further research is recommended to compare MBR accuracy against hand measurements and other methods. The study also emphasises the importance of understanding user attitudes and experiences over time, which can provide valuable insights into the tool’s long-term viability and real impact on CD governance.Overall, the findings suggest that while MBRs have the potential to improve the efficiency of liquid CD checks, their current implementation needs to meet user expectations. Enhanced training and broader availability for different brands may address some identified issues, leading to better user compliance and satisfaction.

Introduction Snoring is an indicator of obstructive sleep apnea (OSA), which contributes to cardiovascular disease and mortality. To better study snoring, audio-based snore detection methods using different feature representations have been proposed. However, there is a gap in (1) baseline comparisons of different deep learning features, and (2) analysis of the robustness of snore detection in the presence of subject variation. Through an ablation study, we quantified the effect of features. As a measure of robustness to subject variation, we employed a leave-one-subject-out scheme. Methods We used 1D raw signals or 2D Mel-frequency-cepstrum-coefficients (MFCC) of the signals as inputs to fully connected, convolutional, long-short-term-memory (LSTM) cell-based recurrent, very deep networks (VGG) or combinations of them. The classifiers were support-vector-machines (SVM) or neural networks. The ablation study consists of seven modular combinations of the elements mentioned above. For training, we used 81,207 snore and non-snore 5s- segments from the snore channel of polysomnography (PSG) data obtained from 19 subjects. A leave-one-subject-out scheme, in which each subject is tested using the training data from other subjects, is used to simulate subject variation. We then measure the variation in performance (F1-score) over different subjects using the standard deviation (SD). Results Features learned from 2D convolutional, LSTM, and very deep network (VGG) significantly improve the classification accuracy and robustness of snore detection. Applying these findings, we developed a 2D convolutional LSTM network model that combines spectral and temporal features, resulting in the highest accuracy (mean F1-score = 0.8812) and the second-best robustness. Very deep convolutional networks (VGG-SVM) has the most robust performance (SD of F1-score = 0.0568). Conclusion We provide a baseline comparison to understand the effect of feature representation on snore classification. Besides accuracy, we introduce robustness as another performance metric. Methods with the best accuracy do not necessarily give the best robustness. Features extracted from 2D-convolutional and LSTM network results in the best accuracy, but those from very deep convolutional networks (VGG) have the best robustness. Support (If Any) Supported by Philips Respironics.

BackgroundThe Clinch Token Transfer Test (C3t) and Step & Stroop Test (SST) are newly developed dual task assessments for Huntington’s Disease (HD). As these are formed of a number of items they produce numerous variables and it is unknown which of these have greatest discriminative ability for each assessment.AimsTo use machine learning classifiers to assess the discriminative ability of the tests and to determine which individual aspects are most important to retain.MethodsControls (n=27) and manifest HD (n=36) participants performed the C3t and SST. The C3t records 30 variables including time to complete each item, number of errors made and task costs (the difference in performance between increasingly complex tasks). The SST records 20 variables including number of steps and Stroop accuracy. To determine the discriminatory power of the assessments two classifiers were constructed, one using variables from the C3t and another using variables from the SST. A feature selection algorithm was used to determine which assessment variables were most important to retain.ResultsThe best performing SST classifier had a mean accuracy of 84% using the number of steps in the baseline Step task, the number of correct answers in the Stroop Congruent Baseline and the number of correct answers in the Stroop Incongruent Baseline. The best performing C3t classifier had a mean accuracy of 88% using the time taken for the C3t Dual Task, the time taken for the C3t Triple Task and the task cost (time) between Baseline and Dual Tasks.ConclusionsThis study suggests that the C3t and SST are reasonably suitable for distinguishing between manifest HD and controls. Furthermore, the assessment complexity can be reduced as the optimal models required a fraction of the scores recorded. Future work will seek to a) reduce the complexity of the tests and b) explore potential test enhancements that may bolster classifier performance.

Introduction: Radiology technologists also known as radiologic technologists or radiographers, are essential members of the modern health care team, utilizing technology and treating people. These skilled employees use diagnostic imaging equipment like X-ray, Computed Tomography (CT) scanners, Magnetic Resonance Imaging (MRI) systems and Ultrasound among others, for creating required diagnostic images, which are important in medical diagnosis and therapeutic planning.Aim of work: To explore the critical role of radiology technologists in enhancing diagnostic accuracy and patient care within the healthcare system.Methods: We conducted a comprehensive search in the MEDLINE database's electronic literature using the following search terms: Role, Radiology, Technologists, Enhancing, Diagnostic, Accuracy and Patient Care. The search was restricted to publications from 2016 to 2024 in order to locate relevant content. We performed a search on Google Scholar to locate and examine academic papers that pertain to my subject matter. The selection of articles was impacted by certain criteria for inclusion.Results: The publications analyzed in this study encompassed from 2016 to 2024. The study was structured into various sections with specific headings in the discussion section.Conclusion: Radiology technologists are very crucial in the health sector since they translate the role of highly technical imaging instruments into the overall excellence in treatment of patients. A pharmacist’s responsibilities include technical knowledge and patient care, team work and professionalism and focus on safety including aspects of professionalism. Through the year, there will always be a challenge and innovation that radiologists will have to tackle and overcome to meet the objectives of improving diagnostic confirmation and care results. Through sponsorship in education, training as well as workplace support, this career field can harness the strengths of radiology technologists within the healthcare setting. Thus, it helps to keep such professionals active, and therefore, continue to provide tremendous value to the constantly innovating area of medical imaging. The path of a radiology technologist is the path of continuous education, teamwork, and contribution to the search for the dinner under talented practicing today as an important element of the improvement of the healthcare industry.