Diagnostic Interpretation Research Articles

Fluorescent bead-based multiplex assays improve serological disease diagnostics and have potential of identifying sensitive immune biomarkers for maintaining health and performance.

Fluorescent bead-based multiplex assays (multiplex assays) for serological detection of antibodies in patient samples have been used in veterinary diagnostics for a little over a decade. These quantitative assays offer several advantages compared to classical serological assays, like a lower limit of detection, less background, and a broader linear quantification range, all of which improve test accuracy. The simultaneous multiplex analysis of a patient's serological response to several specific antigens also improves the diagnostic result interpretation. This influences treatment and management decisions and often allows for a quantitative follow-up as treatment response evaluation. In this review article, we discuss examples of 3 diagnostic multiplex assays for antibody detection in veterinary patients: the Lyme Multiplex assay, the Canine Brucella Multiplex assay, and the Equine Herpesvirus Type-1 Risk Evaluation assay. In addition, multiplex assays for immune response markers, like soluble cytokines, chemokines, or other inflammatory proteins, have recently become available. Currently, these assays are mainly used as clinical research tools to broadly evaluate immune activation and/or inflammation during a variety of infectious and noninfectious diseases. Quantitative cytokine and inflammatory marker multiplex assays have the potential to identify sensitive immune biomarkers for maintaining health and performance in veterinary animals.

Breath-based VOC analysis leveraging canine olfaction for multi-cancer detection: Insights from a 1000-sample study.

1555 Background: Volatile organic compound (VOC) analysis is a validated approach for identifying disease-specific metabolic alterations through exhaled breath. The non-invasive and low-cost nature of breath sample collection makes it particularly suitable for large-scale cancer screening in resource-limited settings, such as those commonly found in the Global South. Canine olfaction has been demonstrated in prior controlled studies to detect VOCs with high accuracy across a range of pathologies, including malignancies. This study evaluates the performance of trained biomedical detection dogs in identifying multiple cancer types using VOC analysis and examines the integration of neurobehavioral data to support real-world diagnostic applications. Methods: A retrospective case-control study was conducted involving 1000 participants across three clinical sites in Hubli, India. Exhaled breath samples (n = 105 cancer-positive, n = 895 healthy controls) were collected using standardized protocols designed to maintain VOC integrity. Trained biomedical detection dogs analyzed these samples, with their behavioral responses recorded via motion sensors, video data, and electroencephalography (EEG) systems. A consensus-based decision framework was implemented to account for variability among individual dogs. Preliminary machine learning models were trained using the recorded neurobehavioral data to evaluate their potential for augmenting detection accuracy; however, these models remain in the validation phase. Results: The detection system demonstrated a sensitivity of 96% and a specificity of 100% across multiple cancer types in the test set, including oral, breast, esophageal, and cervical cancers. Sensitivity for early-stage cancers was 85%. The consensus-based approach among dogs enhanced reliability and minimized individual variability. Preliminary analysis of neurobehavioral data indicates potential for machine learning applications to refine diagnostic interpretation. Conclusions: Breath-based VOC analysis combined with canine olfaction demonstrates high accuracy in multi-cancer detection, including early-stage cancers. Its suitability for non-invasive and low-cost implementation, particularly in resource-constrained settings like the Global South, highlights its potential for addressing disparities in cancer screening access. Future research will focus on validating machine learning models and comparing the system's performance with existing diagnostic standards to further support global scalability and clinical adoption.

A unified framework for residual diagnostics in generalized linear models and beyond

Model diagnostics is an indispensable component in regression analysis, yet it has not been well addressed in generalized linear models (GLMs). When outcome data are discrete, classical Pearson and deviance residuals have limited utility in generating diagnostic insights. This paper establishes a novel diagnostic framework for GLMs and their extensions. Unlike the convention of using a point statistic as a residual, we propose to use a function as a vehicle to retain residual information. In the presence of data discreteness, we show that such a functional residual is appropriate for summarizing the residual randomness that cannot be captured by the structural part of the model. We establish its theoretical properties, which lead to the innovation of new diagnostic tools including the functional-residual-vs-covariate plot and Function-to-Function plot (similar to a Quantile-Quantile plot). Our numerical studies demonstrate that the use of these tools can reveal a variety of model misspecifications, such as not properly including a higher-order term, an explanatory variable, an interaction effect, a dispersion parameter, or a zero-inflation component. As a general notion, the functional residual considerably broadens the diagnostic scope as it applies to GLMs for binary, ordinal and count data as well as semiparametric models (e.g., generalized additive models), all in a unified framework. Its functional form provides a way to unify point residuals such as Liu-Zhang’s surrogate residual and Li-Shepherd’s probability-scale residual. As its graphical outputs can be interpreted in a similar way to those for linear models, our framework also unifies diagnostic interpretation for discrete data and continuous data.

Diagnostic interpretation: Do your test results mean what you think they mean?

In certain situations, diagnostic testing can play an integral role in beef and dairy operations through accurate diagnosis of disease and reduction of disease transmission risk. The qual­ity and applicability of diagnostic results are dependent on the submission of proper samples and appropriate interpretation of what test results mean, along with an understanding of what test results do not mean, within the overall context of the situ­ation. Clinical observations and gross necropsy findings are critical to efficient and accurate diagnostic testing.

Machine Learning for Hand Surgeons: Emerging Clinical Applications.

Machine Learning for Hand Surgeons: Emerging Clinical Applications.

Breast Cancer Surgical Specimens: A Marking Challenge and a Novel Solution-A Prospective, Randomized Study.

Background: Accurate orientation of resected breast specimens is essential for proper pathological evaluation and margin assessment. Misorientation may compromise analysis, lead to imprecise re-excisions, and increase the risk of local recurrence. This study aims to evaluate a novel specimen plate designed to maintain consistent tissue orientation and compares its effectiveness to traditional suture marking. Methods: In a single-center, prospective, randomized two-arm trial, 56 specimens were oriented with the new plate and 54 with conventional sutures. Outcomes included intraoperative imaging interpretation, specimen handling, and pathological assessment, with a focus on orientation accuracy and margin evaluation. Results: The specimen plate significantly reduced misorientation (p < 0.01) and improved interpretation during intraoperative imaging. Pathologists reported greater ease in identifying direction and tumor-free zones, leading to a more accurate margin assessment. Non-R0 resections requiring re-excision were fewer with the specimen plate (8.9%) compared to suture marking (22.2%). Conclusions: The newly developed specimen plate can offer a reliable solution for improving specimen orientation in breast cancer surgery; however, further validation in multicenter studies is needed to confirm its applicability across diverse surgical settings. By ensuring consistent orientation and enhancing diagnostic interpretation, it may help reduce re-excisions and improve patient safety.

Artificial Intelligence for Low-Dose CT Lung Cancer Screening: Comparison of Utilization Scenarios.

Background: Artificial intelligence (AI) tools for evaluating low-dose CT (LDCT) lung cancer screening examinations are used predominantly for assisting radiologists' interpretations. Alternate utilization scenarios (e.g., use of AI as a prescreener or backup) warrant consideration. Objective: To evaluate the impact of different AI utilization scenarios on diagnostic outcomes and interpretation times for LDCT lung cancer screening. Methods: This retrospective study included 366 individuals (358 male, 8 female; mean age, 64 years) who underwent LDCT from May 2017 to December 2017 as part of an earlier prospective lung cancer screening trial. Examinations were interpreted by one of five readers, who reviewed their assigned cases in two sessions (with and without a commercial AI computer-aided detection tool). These interpretations were used to reconstruct simulated AI utilization scenarios: assistant-radiologists interpret all examinations with AI assistance; prescreener-radiologists only interpret examinations with a positive AI result; backup-radiologists reinterpret examinations when AI suggests a missed finding. A group of thoracic radiologists determined the reference standard. Diagnostic outcomes and mean interpretation times were assessed. Decision curve analysis was performed. Results: Compared to interpretation without AI (recall rate, 22.1%; per-nodule sensitivity, 64.2%; per-examination specificity, 88.8%; mean interpretation time, 164 seconds), AI as an assistant showed higher recall rate (30.3%; P<.001), lower per-examination specificity (81.1%), and no significant change in per-nodule sensitivity (64.8%; P=.86) or mean interpretation time (161 seconds; P=.48); AI as a prescreener showed lower recall rate (20.8%; P=.02) and mean interpretation time (143 seconds; P<.001), higher per-examination specificity (90.3%, P=.04), and no significant difference in per-nodule sensitivity (62.9%; P=.16); and AI as a backup showed increased recall rate (33.6%; P<.001), per-examination sensitivity (66.4%; P<.001), and mean interpretation time (225 seconds; P=.001), with lower per-examination specificity (79.9%; P<.001). Among scenarios, only AI as a prescreener demonstrated higher net benefit than interpretation without AI; AI as an assistant had least net benefit. Conclusion: Different AI implementation approaches yield varying outcomes. The findings support use of AI as a prescreener as the preferred scenario. Clinical Impact: An approach whereby radiologists only interpret LDCT examinations with a positive AI result can reduce radiologists' workload while preserving sensitivity.

Diagnostic performance of advanced large language models in cystoscopy: evidence from a retrospective study and clinical cases

PurposeTo evaluate the diagnostic capabilities of advanced large language models (LLMs) in interpreting cystoscopy images for the identification of common urological conditions.Materials and methodsA retrospective analysis was conducted on 603 cystoscopy images obtained from 101 procedures. Two advanced LLMs, both at the forefront of artificial intelligence technology, were employed to interpret these images. The diagnostic interpretations generated by these LLMs were systematically compared against standard clinical diagnostic assessments. The study’s primary outcome measure was the overall diagnostic accuracy of the LLMs. Secondary outcomes focused on evaluating condition-specific accuracies across various urological conditions.ResultsThe combined diagnostic accuracy of both LLMs was 89.2%, with ChatGPT-4 V and Claude 3.5 Sonnet achieving accuracies of 82.8% and 79.8%, respectively. Condition-specific accuracies varied considerably, for specific urological disorders: bladder tumors (ChatGPT-4 V: 92.2%, Claude 3.5 Sonnet: 80.9%), BPH (35.3%, 32.4%), cystitis (94.5%, 98.9%), bladder diverticula (92.3%, 53.8%), and bladder trabeculae (55.8%, 59.6%). As for normal anatomical structures: ureteral orifice (ChatGPT-4 V: 48.8%, Claude 3.5 Sonnet: 61.0%), bladder neck (97.9%, 93.8%), and prostatic urethra (64.3%,57.1%).ConclusionsAdvanced language models demonstrated varying levels of diagnostic accuracy in cystoscopy image interpretation, excelling in cystitis detection while showing lower accuracy for other conditions, notably benign prostatic hyperplasia. These findings suggest promising potential for LLMs as supportive tools in urological diagnosis, particularly for urologists in training or early career stages. This study underscores the need for continued research and development to optimize these AI-driven tools, with the ultimate goal of improving diagnostic accuracy and efficiency in urological practice.Clinical trial numberNot applicable.

3D lymphoma segmentation on PET/CT images via multi-scale information fusion with cross-attention.

Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Traditional methods often struggle to delineate these lesions accurately. This study aims to develop a precise segmentation method for DLBCL using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and computed tomography (CT) images. We propose a 3D segmentation method based on an encoder-decoder architecture. The encoder incorporates a dual-branch design based on the shifted window transformer to extract features from both PET and CT modalities. To enhance feature integration, we introduce a multi-scale information fusion (MSIF) module that performs multi-scale feature fusion using cross-attention mechanisms with a shifted window framework. A gated neural network within the MSIF module dynamically adjusts feature weights to balance the contributions from each modality. The model is optimized using the dice similarity coefficient (DSC) loss function, minimizing discrepancies between the model prediction and ground truth. Additionally, we computed the total metabolic tumor volume (TMTV) and performed statistical analyses on the results. The model was trained and validated on a private dataset of 165 DLBCL patients and a publicly available dataset (autoPET) containing 145 PET/CT scans of lymphoma patients. Both datasets were analyzed using five-fold cross-validation. On the private dataset, our model achieved a DSC of 0.7512, sensitivity of 0.7548, precision of 0.7611, an average surface distance (ASD) of 3.61 mm, and a Hausdorff distance at the 95th percentile (HD95) of 15.25 mm. On the autoPET dataset, the model achieved a DSC of 0.7441, sensitivity of 0.7573, precision of 0.7427, ASD of 5.83 mm, and HD95 of 21.27 mm, outperforming state-of-the-art methods (p<0.05, t-test). For TMTV quantification, Pearson correlation coefficients of 0.91 (private dataset) and 0.86 (autoPET) were observed, with R2 values of 0.89 and 0.75, respectively. Extensive ablation studies demonstrated the MSIF module's contribution to enhanced segmentation accuracy. This study presents an effective automatic segmentation method for DLBCL that leverages the complementary strengths of PET and CT imaging. The method demonstrates robust performance on both private and publicly available datasets, ensuring its reliability and generalizability. Our method provides clinicians with more precise tumor delineation, which can improve the accuracy of diagnostic interpretations and assist in treatment planning for DLBCL patients. The code for the proposed method is available at https://github.com/chenzhao2023/lymphoma_seg.

Classic Hodgkin Lymphoma With Primary Presentation as Lytic Bone Lesions and Pancytopenia: Report of a Pediatric Case and Review of Literature.

In this report, we describe a case of classic Hodgkin lymphoma presenting with lytic bone lesions and pancytopenia, but with no significant lymphadenopathy or mediastinal mass. We report detailed clinical, radiologic, and pathologic findings. We discuss the scant medical literature of similar cases. We conclude that such cases often represent diagnostic challenges at the clinical and microscopic levels. We emphasize that awareness of this rare presentation of Hodgkin lymphoma is key to avoid diagnostic delay or interpretation pitfalls.

Kontribusi Kompetensi Dignostik dan Komunikasi Interpersonal Pelayanan Pastoral terhadap Sikap Beriman Kaum Muda dalam Gereja

The pastor plays various important roles in the congregation, such as preacher, teacher, priest, administrator, and counselor. Among all these roles, the counselor holds the greatest responsibility. The task of pastoral counseling includes providing emotional and spiritual support to the congregation. In addition to helping the congregation overcome emotional, mental, and spiritual issues, counseling also assists them in discovering their identity, life calling, and potential, so that they can develop holistically in life. Pastoral counseling is a form of interpersonal communication that requires the pastor to have good communication skills. The ability to build strong relationships with the congregation significantly influences their spiritual development. Additionally, empathy and compassion are qualities that pastors must possess to support their interpersonal skills. Diagnostic skills are also crucial for a pastoral counselor. With these skills, the counselor can identify the problems faced by the congregation and provide assistance or refer them for further treatment. Interpersonal skills help build open relationships, while diagnostic abilities are essential in identifying the root causes of the congregation's issues. In the diagnostic process, assessment and interpretation skills are vital to understanding the congregation's problems comprehensively—emotionally, mentally, socially, culturally, and spiritually—so that the solutions provided can be effective and accurate. Pastoral counseling plays a significant role in supporting the growth of faith and overall well-being of the congregation.

Expert consensus on clinical genome sequencing interpretation and reporting.

Genome sequencing (GS) refers to a technology that comprehensively and systematically detects the DNA sequences of an individual's nuclear and mitochondrial genomes. It aims to identify genetic variants and investigate their roles in human health and disease progression. As an emerging diagnostic tool, GS offers significant support for clinical diagnosis due to its high throughput, accuracy, and comprehensiveness. However, the complexity of data analysis and interpretation requires substantial professional expertise and experience, posing considerable challenges. When applying GS technology for molecular diagnosis of genetic diseases, ethical and technical issues related to clinical application arise, including informed consent, diagnostic data interpretation, and defining the scope and content of clinical reports. This expert consensus outlines the core workflow of clinical genome sequencing (cGS), clarifies its testing scope and technical limitations, and provides key steps for data quality control, analysis, annotation, and variant interpretation. It also addresses controversial issues related to report content and informed consent. This consensus aims to assist professionals in accurately understanding and appropriately utilizing clinical genome sequencing, thereby improving diagnostic accuracy for genetic diseases, enhancing the clinical utility of the technology, and advancing medical scientific research.

1553 Enhancing Diagnostic Interpretation in Histopathological Image Analysis through a Cluster Attention Method

1553 Enhancing Diagnostic Interpretation in Histopathological Image Analysis through a Cluster Attention Method

MRI after Cervical Spine Decompression and Fusion Surgery: Technical Considerations, Expected Findings, and Complications.

Cervical spine MRI is essential for evaluating potential complications and symptomatic degenerative changes following cervical decompression and fusion surgery. High-yield diagnostic interpretation considers the underlying surgical approach (anterior vs posterior), the time elapsed since surgery, and the clinical status of the patient to reliably differentiate expected postoperative changes from surgical complications. As cervical anatomy, such as the foramina and nerve roots, is smaller than that of the lumbar spine, MRI acquisition challenges include the demand for higher spatial resolution. Another unique challenge for cervical spine MRI is susceptibility to motion artifacts from swallowing, breathing, and cerebrospinal fluid pulsation. Modified MRI protocols, including the use of metal artifact suppression techniques, can help mitigate susceptibility artifacts from metallic implants. This focused review of postoperative cervical spine MRI discusses common cervical surgery decompression and fusion approaches and recommended MRI acquisition and interpretation algorithms, briefly considers radiofrequency coil selection, and illustrates complications in both early and delayed phases.

Multi-antigen serology and a diagnostic algorithm for the detection of arbovirus infections as novel tools for arbovirus preparedness in southeast Europe (MERMAIDS-ARBO): a prospective observational study.

Arboviruses are increasingly affecting Europe, partly due to the effects of climate change. This increase in range and impact emphasises the need to improve preparedness for emerging arboviral infections that often co-circulate and might have overlapping clinical syndromes. We aimed to strengthen surveillance networks for four clinically relevant arboviruses in southeast Europe. This study reports an in-depth analysis of the MERMAIDS-ARBO prospective observational study in adults (ie, aged ≥18 years) hospitalised with an arbovirus-compatible disease syndrome in 21 hospitals in seven countries in southeast Europe over four arbovirus seasons (May 1-Oct 31, 2016-19) to obtain arbovirus prevalence outcomes. The main objectives of the MERMAIDS-ARBO study, describing the clinical management and outcomes of four arboviruses endemic to southeast Europe, including Crimean-Congo haemorrhagic fever virus (CCHFV), tick-borne encephalitis virus (TBEV), Toscana virus, and West Nile virus (WNV), are reported elsewhere. In this analysis, given the challenges associated with arbovirus diagnostics, we developed a diagnostic algorithm accounting for serology outcomes and sample timing to study arbovirus prevalence in southeast Europe. Serum samples were collected on days 0, 7, 28, and 60 after hospital admission and tested for anti-CCHFV IgG and IgM antibodies with ELISAs (confirmed with an indirect immunofluorescence test) and for IgG and IgM antibodies specific to TBEV, Toscana virus, and WNV with custom-printed protein microarrays (confirmed with virus neutralisation tests). All acute-phase samples were tested by PCR for all four viruses. Descriptive analyses were performed for virus-reactive cases by geography and year, and possible factors (eg, age, sex, and insect bites) associated with virus reactivity were assessed. Of 2896 individuals screened, 913 were eligible for inclusion, of whom 863 (514 men, 332 women, and 17 unknown) had samples sent to the study reference laboratories and were included in molecular and serological analyses. Some individuals had insufficient clinical data to be included in the clinical analysis, but met the eligibility criteria for and were included here. Serum sampling was incomplete (eg, samples missing from one or more timepoints or no data on time since symptom onset) for 602 (70%) patients, and the timing of collection was often heterogeneous after symptom onset up to 40 days (average median delay of 5-6 days across all timepoints), affecting the ability to diagnose arbovirus infection by serology. By use of an interpretation table incorporating timing and completeness of sampling, one (<1%) participant had a confirmed recent infection with CCHFV, ten (1%) with TBEV, 40 (5%) with Toscana virus, and 52 (6%) with WNV. Most acute confirmed infections of Toscana virus were found in Albania (25 [63%] of 40), whereas WNV was primarily identified in Romania (36 [69%] of 52). Albania also had the highest overall Toscana virus seropositivity (168 [60%] of 282), mainly explained by patients confirmed to be exposed or previously exposed (104 [62%] of 168). Patients without antibodies to WNV or Toscana virus were significantly younger than patients with antibodies (mean difference -8·48 years [95% CI -12·31 to -4·64] for WNV, and -6·97 years [-9·59 to -4·35] for Toscana virus). We found higher odds of Toscana virus reactivity in men (odds ratio 1·56 [95% CI 1·15 to 2·11]; p=0·0055), WNV reactivity with mosquito bites versus no mosquito bites (2·47 [1·54 to 3·97]; p=0·0002), and TBEV reactivity with tick bites versus no tick bites (2·21 [1·19 to 4·11]; p=0·018). This study shows that despite incomplete and heterogeneous data, differential diagnosis of suspected arbovirus infections is possible, and the diagnostic interpretation algorithm we propose could potentially be used to strengthen routine diagnostics in clinical settings in areas at risk for arboviral diseases. Our data highlight potential hotspots for arbovirus surveillance and risk factors associated with these particular arbovirus infections. European Commission and Versatile Emerging infectious disease Observatory. For the Greek, Albanian, Romanian, Bosnian, Serbian, and Croatian translation of the summary see Supplementary Materials section.

INCIDENTAL FINDINGS IN PANORAMIC RADIOGRAPHS: A DESCRIPTIVE STUDY OF PANORAMIC RADIOGRAPHS TAKEN ON X HOSPITAL IN JAMBI CITY INDONESIA

Abnormalities without symptoms can be detected through accurate diagnostic interpretation techniques in a wide range of panoramic radiographs. The number of publications describing various incidental findings on panoramic radiographs in Indonesia is still limited. The aim of this study is to find incidental findings in panoramic radiographs. The design of this study is descriptive, using 962 panoramic radiographs. A total of two observers interpreted 481 radiographs each and recorded incidental findings into five categories, namely soft tissue calcification, elongation of the styloid process, pathological conditions of the maxillary sinus, dense bone islands, and other incidental findings. The results shows that 142 panoramic radiographs (14,76%) had images of incidental findings, with descriptions of the types of incidental findings that were found are 42 radiographs (29,57%) of soft tissue calcification, 29 radiographs (20,42%) had an elongation of the styloid process, pathological conditions of the maxillary sinus were found on 35 radiographs (24,64%), 32 radiographs (22,53%) of dense bone island, and 17 radiographs (11,97%) were categorized as other incidental findings. The percentage of incidental findings which is not too high (14,76%), does not affect the fact that it is very important for a dentist to interpret panoramic radiographs in such detail manners and be alert of various pathological conditions that appear even without clinical symptoms, and ultimately be able to provide external referrals so that early medical intervention can be carried out in patients who needs it the most.

Simulator transesophageal echocardiography: the new horizon of training?

Abstract Background trans-esophageal echocardiography (TEE) plays a crucial role in the diagnosis of cardiovascular diseases. In recent years, the importance of theoretical-practical training based on advanced simulation systems in learning specific medical skills has emerged but evidence for the effectiveness for TEE are limited. Purpose evaluate the impact of a theoretical-practical course with simulator for TEE in the diagnostic interpretation of morpho-functional images and acquisition of projections according to standard protocol of the American Society of Echocardiography (ASE). Methods cardiologist specialists and resident cardiologists who participated in the TEE course at a center of simulation and advanced training center from 2018 to 2023, were retrospectively enrolled in the study. Before the course, participants underwent a question multiple-choice based test, aimed at assessing theoretical knowledge and clinical interpretation of morpho-functional images and clips recorded during interventional procedures. During the course, learners where divided into three groups: one utilizing the simulator to conduct exams with clinical cases and acquisition of clips according to ASE protocol, another one participating in interactive discussions of the first group's TEE hands-on activities, and the last one attending lectures with clinical cases. The activities were switched during the two days of the course. Following the course, the same test was administered to evaluate changes in results. Results 290 hospital cardiologists and resident cardiologists (95.2% and 4.8%, respectively) participated in the course. Among them, mean age was 45.2 ± 11 years old, 164 were male (57%). Before the course, the learners answered correctly the 77.6% of questions, while after the course this value increase to 89.5% (p value &amp;lt; 0.001). This improved result was driven by the group above 54 years (p = 0.026) and by the one with an age between 45 and 54 years (p = 0.042), as shown in Figure 1. 205 learners (71.9%) improved their test results after the course, while 41 (14.4%) lowered the score and 39 (13.7%) achieved the same result. In the learners that improved (205), 79% wronged ≥3 questions at the first evaluation and after the course this number decreased to 15% (p &amp;lt; 0.001); on the other hand, in the ones that not improved (80), 14% wronged ≥ 3 questions and this value grew to 45% after the course (p &amp;lt; 0.001), as shown in Figure 2. Moreover, at the end of the training, all the learners were able to complete the sequence of 28 projections of the standard protocol of trans-esophageal echocardiographic recommended by international society of echocardiography. Conclusions In our cohort of cardiologist specialists and fellows, a two-day theoretical-practical course with a TEE-based simulator improved competencies in interpretation of TEE clips and images. These findings should therefore encourage greater use of advanced training with simulation TEE training.

Assessing misconceptions in astronomy: The use of ordered multiple-choice items

This study pilots a unique test item known as the ordered multiple-choice (OMC) item. These OMC items were administered to two high school astronomy classrooms participating in a &lt;i&gt;NASA classroom of the future: Astronomy village&lt;/i&gt; program. The OMC items were included on a pre- and post-test to assess common misconceptions in astronomy, as part of study employing a quasi-experimental design. Each answer choice in an OMC item is linked to varying levels of student understanding, allowing for diagnostic interpretation of student responses. Results from the items indicated that although students did improve in their levels of understanding at the end of the program, there were still gaps in the students’ knowledge. We anticipate these items will allow for a unique and comprehensive assessment of student understanding of science.

Real-Time Deployment of Ultrasound Image Interpretation AI Models for Emergency Medicine Triage Using a Swine Model

Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the diagnostic accuracy of an eFAST exam depends on obtaining proper scans and making quick interpretation decisions to evacuate casualties or administer necessary interventions. To improve ultrasound interpretation, we developed AI models to identify key anatomical structures at eFAST scan sites, simplifying image acquisition by assisting with proper probe placement. These models plus image interpretation diagnostic models were paired with two real-time eFAST implementations. The first implementation was a manual AI-driven ultrasound eFAST tool that used guidance models to select correct frames prior to making any diagnostic predictions. The second implementation was a robotic imaging platform capable of providing semi-autonomous image acquisition combined with diagnostic image interpretation. We highlight the use of both real-time approaches in a swine injury model and compare their performance of this emergency medicine application. In conclusion, AI can be deployed in real time to provide rapid triage decisions, lowering the skill threshold for ultrasound imaging at or near the point of injury.

Non-Invasive Determination of the Paternal Inheritance in Pregnancies at Risk for β-Thalassaemia by Analyzing Cell-Free Fetal DNA Using Targeted Next-Generation Sequencing.

Non-invasive prenatal testing (NIPT) has been widely adopted for the screening of chromosomal abnormalities; however, its adoption for monogenic disorders, such as β-thalassaemia, has proven challenging. Haemoglobinopathies are the most common monogenic disorders globally, with β-thalassaemia being particularly prevalent in Cyprus. This study introduces a non-invasive prenatal haplotyping (NIPH) assay for β-thalassaemia, utilizing cell-free DNA (cfDNA) from maternal plasma. The assay determines paternal inheritance by analyzing highly heterozygous single-nucleotide variants (SNVs) in the β-globin gene cluster. To identify highly heterozygous SNVs in the population, 96 randomly selected samples were processed using Illumina DNA-prep NGS chemistry. A custom, high-density NGS genotyping panel, named HAPLONID, was designed with 169 SNVs, including 15 common pathogenic ones. The AmpliSeq for Illumina assay was then applied to cfDNA to evaluate the panel's efficiency in performing NIPT for β-thalassaemia. Analysis revealed 219 highly polymorphic SNVs, and the sequencing of 17 families confirmed successful paternal allele determination. The NIPH assay demonstrated 100% success in diagnostic interpretation. This study achieved the advancement of an integrated NGS-NIPT assay for β-thalassaemia, bringing it one step closer to being a diagnostic assay and thereby enabling a reduction in the number of risky invasive prenatal sampling procedures in Cyprus and elsewhere.