Diagnostic Accuracy Research Articles

Current Status of Magnetic Resonance Imaging Use in Bladder Cancer.

Bladder cancer (BC) is a significant global health concern, with over 500,000 new cases and 200,000 deaths annually, emphasizing the need for accurate staging and effective management. Traditional diagnostic techniques, such as cystoscopy and transurethral resection, are fundamental but have limitations in accurately assessing the depth of invasion. These limitations include the possibility of understaging and procedural variability, which can significantly impact treatment decisions. This review focuses on the role of multiparametric magnetic resonance imaging (mpMRI) in the diagnosis and staging of BC, particularly emphasizing the Vesical Imaging-Reporting and Data System (VI-RADS) framework. By enhancing interpretive consistency and diagnostic accuracy, mpMRI and VI-RADS offer detailed visualization of tumor characteristics and depth of invasion, while reducing the need for more invasive traditional methods. These advancements not only improve staging accuracy but also enhance treatment planning, underscoring the importance of advanced imaging in evolving BC management and positively influencing patient outcomes.

Minimum Fascia-Tumour Distance for Differentiating Deep Lobe From Superficial Lobe Benign Parotid Tumours: A Retrospective Study and Meta-Analysis.

This study was designed to evaluate the diagnostic efficacy of the minimum fascia-tumour distance (MFTD) in distinguishing deep-lobe benign parotid tumours from superficial-lobe tumours through both an original study and a meta-analysis. In this study, we performed a retrospective analysis of data from 91 patients who had been diagnosed with benign parotid tumours. The MFTD values were sourced from preoperative ultrasound examinations. The locations of these tumours were confirmed through surgical findings. We assessed the diagnostic accuracy of MFTD by utilising receiver operating characteristic (ROC) curves. Additionally, we conducted a systematic review of the pertinent literature and performed a diagnostic meta-analysis to ascertain the overall diagnostic efficacy of MFTD in identifying benign parotid tumours. Patients with tumours in the deep lobe had a significantly greater MFTD than patients with tumours in the superficial lobe. Using a cutoff value of 3.50 mm for MFTD, we found an AUC of 0.93, a sensitivity of 81.8%, and a specificity of 98.8%. Our meta-analysis included seven studies covering a total of 1689 tumours. The pooled values for sensitivity, specificity, and diagnostic odds ratio (OR) of MFTD were 81.0%, 89.0%, and 32.2, respectively. The AUC of the summarised ROC curve of MFTD was 0.90. The MFTD demonstrated reliable diagnostic accuracy in identifying deep-lobe benign parotid tumours and may be incorporated into standard evaluations before parotidectomy.

The undervalued league of insulin resistance testing: uncovering their importance.

Type 2 diabetes, obesity, and several other metabolic diseases are all largely attributed to the problem known as insulin resistance. Diagnosing insulin resistance promptly and accurately is essential for adequately managing and intervening in metabolic disorders. Several diagnostic methods have been developed to assess insulin resistance. However, each method has advantages and disadvantages. The most precise test is the hyperinsulinemic-euglycemic clamp, which examines the direct impact of insulin on glucose uptake by tissues. However, it is primarily utilized in research due to its complexity and intrusiveness. Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and the Quantitative Insulin Sensitivity Check Index (QUICKI) are the second most used Insulin resistance tests in the clinical setup. These tests are based on measuring the fasting glucose and insulin levels. The Oral Glucose Tolerance Test (OGTT), Insulin tolerance test, and the Matsuda Index are further diagnostic procedures that shed light on insulin sensitivity. The improved techniques, such as the insulin suppression test and the minimal model analysis, provide substitutes for unique clinical circumstances. Additionally, including extra measurements with these tests, like waist circumference, lipid profiles, and inflammatory markers, can improve the evaluation of insulin resistance. In summary, identifying insulin resistance is essential for the early detection and treatment of various metabolic illnesses. To make educated judgments and improve patient care, healthcare workers should be aware of thedifferent available diagnostic tests and how they are used in each situation. Insulin resistance detection and monitoring will require further study to improve current diagnostic approaches and create novel, less invasive techniques.

Cervical Auscultation for Detecting Oropharyngeal Aspiration in Paediatric and Adult Populations: A Systematic Review and Meta-Analysis.

Cervical auscultation (CA) involves listening to swallowing and respiratory sounds and/or vibrations to detect oropharyngeal aspiration (OPA). CA has shown promising diagnostic test accuracy when used with the clinical swallowing examination and is gaining popularity in clinical practise. There has not been a review to date analysing the accuracy of CA in paediatric and adult populations with meta-analyses. To determine the accuracy of CA in detecting OPA in paediatric and adult populations, when compared to instrumental assessments. Databases searched included MEDLINE, PubMed, Embase, CINAHL, AustHealth, Cochrane and Web of Science. The search was restricted between 01 October 2012 and 01 October 2022. Inclusion criteria included (a) all clinical populations of all ages, (b) who have had an instrumental assessment and (c) CA. All study types were included. Studies were reviewed independently by two authors. The methodological quality of the studies was analysed using the QUADAS-2. Ten studies met the inclusion criteria for this review and meta-analyses. The pooled diagnostic performance of CA in detecting OPA was 0.91 for sensitivity and 0.79 for specificity. The area under the curve summary receiver operating curve (sROC) was estimated to be 0.86, thereby indicating good discrimination of OPA. Most studies scored high for risk of bias in at least one domain in the QUADAS-2, likely attributed to a lack of high-quality prospectively designed studies. There are promising diagnostic test accuracies for the use of CA in detection of OPA. Future research could include using CA in specific clinical populations and settings, and identifying standardised criteria for CA.

Evaluation of truenat assays for the diagnosis of pulmonary and extrapulmonary tuberculosis: a systematic review and meta-analysis.

Tuberculosis (TB) remains a significant global health concern, necessitating accurate and timely diagnostic methods. This study aimed to conduct a systematic review and meta-analysis to assess the diagnostic accuracy of Truenat assays for both pulmonary TB (PTB) and extrapulmonary TB (EPTB). PubMed, Scopus, and Web of Science were systematically searched for studies comparing Truenat assays to Mycobacterium tuberculosis confirmation methods. Comparing Truenat MTB assay with Lowenstein-Jensen (LJ) culture, the pooled sensitivity and specificity were 86% (95% CI: 79-91%) and 86% (95% CI: 82-90%), respectively. For Mycobacterial Growth Indicator Tube (MGIT) culture comparison, pooled sensitivity was 88% (95% CI: 82-92%) with a specificity of 79% (95% CI: 57-92%). Compared to smear microscopy, Truenat assays displayed the pooled sensitivity and specificity of 92% (95% CI: 78-98%) and 86% (95% CI: 64-95%). In comparison to Xpert MTB/RIF, Truenat assays exhibited a pooled sensitivity of 92% (95% CI: 80-97%) and a pooled specificity of 92% (95% CI: 56-99%) for PTB detection, and a pooled sensitivity of 94% (95% CI: 81-98%) and a specificity of 77% (95% CI: 32-96%) for the diagnosis of EPTB. This study underscores the potential of Truenat assays as valuable tools for diagnosing both PTB and EPTB. CRD42024526686.

Global Study on the Accuracy of Human Epidermal Growth Factor Receptor 2-Low Diagnosis in Breast Cancer.

Recently, a new type of antibody-drug conjugate, trastuzumab-deruxtecan (T-DXd), has been approved for the treatment of metastatic breast cancer with low level of human epidermal growth factor receptor 2 (HER2) gene expression. Thereby, eligibility relies on an accurate diagnosis of HER2-low status defined by immunohistochemistry IHC 1+/2+ with no gene amplification. To assess pathologists' accuracy and training efficacy in the diagnosis of HER2-low. Agreement rates of HER2-low scoring in breast cancer tissue were assessed between expert consensus and real-world pathologists (n = 77 from 14 countries) before and after a specific 4-hour training for HER2-low detection. Two assays were evaluated, the Ventana Pathway 4B5 CDx and the Dako HercepTest (polyclonal). Concordance of the pathologists with consensus score and efficacy of training were measured by Cohen κ, overall rater agreement, and receiver operating characteristic (ROC) curve statistics. In the Ventana 4B5 HER2-low category, baseline agreement rates were >80% but <90%. Negative percentage agreement was improved from 80.6% to 91.1% by training. In the HER2-0 category, positive percentage agreement (74.6%) was the only parameter below the 80% benchmark but was significantly improved to 89.2% after training. Training efficacy was confirmed by ROC curve analysis, which shows improvement for the identification of HER2-0 and HER2-low cases. Finally, in-depth examination of cases with discordant HER2 status disclosed specific issues of HER2-low underscoring and overscoring. The ability of pathologists to achieve acceptable diagnostic accuracy in identifying patients with HER2-low breast cancer could be enhanced by short-term training. Potential routes to improve the quality of HER2-low scoring in clinical practice have been identified.

Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review.

Artificial intelligence (AI) is a rapidly growing field of computational research with the potential to extract nuanced biomarkers for the prediction of outcomes of interest. AI implementations for the prediction for clinical outcomes for myeloproliferative neoplasms (MPNs) are currently under investigation. In this narrative review, we discuss the AI investigations for the improvement of MPN clinical care utilizing either clinically available data or experimental laboratory findings. Abstracts and manuscripts were identified upon querying PubMed and the American Society of Hematology conference between 2000 and 2023. Overall, multidisciplinary researchers have developed AI methods in MPNs attempting to improve diagnostic accuracy, risk prediction, therapy selection, or pre-clinical investigations to identify candidate molecules as novel therapeutic agents. It is our expert opinion that AI methods in MPN care and hematology will continue to grow with increasing clinical utility. We believe that AI models will assist healthcare workers as clinical decision support tools if appropriately developed with AI-specific regulatory guidelines. Though the reported findings in this review are early investigations for AI in MPNs, the collective work developed by the research community provides a promising framework for improving decision making in the future of MPN clinical care.

Pitfalls in the Cytological Diagnosis of Nodal Hodgkin Lymphoma.

Hodgkin lymphoma (HL) is a hematopoietic neoplasm characterized by malignant Reed-Sternberg (RS) cells in an inflammatory background. Although the cytological features of HL are well elucidated in literature, yet many postulated factors cause its misdiagnosis. This study aims to assess the diagnostic reliability of fine needle aspiration cytology (FNAC) in HL and evaluate the factors contributing to a false-negative and false-positive diagnosis, taking histopathology as the gold standard. This was a retrospective study in which 47 cases of HL diagnosed on histopathology were compared with their prior cytological diagnosis. The patient's age ranged from 3 to 80 years (median: 36 years) with a M:F ratio of 2.9:1. Lymph node aspirations were performed from multiple anatomical sites, out of which the cervical was the most common (57.8%). FNAC was inconclusive in two cases due to unsatisfactory smears. The false-negative diagnosis of reactive lymphadenitis was given in four cases, and false-positive in four cases, which included three cases of non-HL, and one case of malignant small round blue cell tumor. The overall diagnostic accuracy of FNAC in the diagnosis of HL was 82.2%. The cytological diagnosis of HL can be challenging when classic RS cells are absent. Contributing factors for a false-negative diagnosis include obscuring reactive inflammatory cells, fibrosis of the involved lymph nodes, partial involvement of the lymph node, and misinterpretation. A thorough clinical examination with evaluation of FNAC smears from multiple areas, and ancillary tests help improve the diagnostic accuracy of cytological diagnosis.

Visualization and characterization of complement activation in acetylcholine receptor antibody seropositive myasthenia gravis.

There are no blood biomarkers to monitor treatment effects in myasthenia gravis (MG) or studies visualizing the acetylcholine receptor (AChR) antibody-induced membrane attack complex (MAC) at the human muscle membrane. This study aimed to compare levels of complement activation products and native complement components in MG patients and healthy controls (HCs) and to model the AChR antibody-mediated attacks in human muscle cells. We assessed the complement components and activation product levels with enzyme-linked immunosorbent assay and magnetic bead-based sandwich assays in plasma and sera of 23 MG patients and matched HCs. Receiver operator characteristic (ROC) curve analysis evaluated the diagnostic accuracy. Complement levels were correlated with the myasthenia gravis composite (MGC) scores. AChR+ MG modeling in human muscle cells used sera from nine MG patients and three HCs. MG patients had significantly higher plasma levels of C3a (p < .0001), C5 (p = .0003), and soluble C5b-9 (sC5b-9; p < .0001) than HCs. The ROC curve analysis showed a clear separation between MG patients and HCs for plasma C3a (AUC = 0.9720; p < .0001) and sC5b-9 (AUC = 0.8917, p < .0001). MG patients had higher levels of plasma complement Factor I (FI; p = .0002) and lower properdin levels (p < .0001). The MGC had moderate correlations with plasma Factor B (FB), FI, and Factor H. AChR+ MG patient sera triggered the deposition of MAC and reduced AChRs. We suggest validating plasma C3a and sC5b-9 as blood biomarkers for complement activation in MG. Further, the in vitro study allowed visualization of MAC deposition after applying AChR+ MG sera on human muscle cells.

Can clinicians identify community-acquired pneumonia on ultralow-dose CT? A diagnostic accuracy study

BackgroundWithout increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians’ assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist’s assessments as reference standard.MethodsThis was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations.ResultsAll clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians’ CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77–88), specificity = 70% (95%CI: 59–81), positive predictive value = 80% (95%CI: 74–84), negative predictive value = 78% (95%CI: 73–82).ConclusionThis study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

Comparison of the modified Masood's scoring index versus international academy of cytology Yokohama system in the categorization of breast fine needle aspirates.

The Modified Masood Scoring Index (MMSI) categorizes breast fine needle aspirates into four categories non-proliferative breast diseases (PBD), PBD without atypia, PBD with atypia and carcinoma insitu/carcinoma. The International Academy of Cytology Yokohama System classifies the aspirates into five categories - inadequate, benign, atypical, suspicious, and malignant. Very few studies have been conducted so far to compare the diagnostic accuracy of this system. The objectives of the study were to classify breast fine needle aspirates according to the MMSI and IAC Yokohama and assess their risk of malignancy (ROM) and performance parameters. All breast FNAs received from June 2020 to May 2023 were classified according to the MMSI and IAC Yokohama. Using histopathological diagnosis as the gold standard, ROM and performance parameters were calculated. Out of the 559 breast fine needle aspirates, the histopathological correlation was available for 337 cases. The ROM for non-PBD, PBD without atypia, PBD with atypia and carcinoma insitu/carcinoma categories were 0%, 1.2%, 9.1% and 93%, respectively. The ROM for each of the categories of Yokohama system was 16.6%, 1.1%, 4.3%, 86.2% and 97.6%, respectively. The values for the sensitivity, specificity, PPV, NPV and diagnostic accuracy were slightly higher for IAC Yokohama (97.3%, 97.2%, 94.7%, 98.6% and 97.2%, respectively) than the MMSI (96.4%, 96.2%, 93%, 98.1% and 96.3%, respectively). Furthermore, another shortfall of MMSI is the inability to compare the Inadequate category of the Yokohama system. Overall Yokohama system proved to be a better system for categorizing breast lesions on FNAB as the scoring system provides more objective categorization and minimizes false positive and false negative cases.

Measuring the operational performance of an artificial intelligence-based blood tube-labeling robot, NESLI.

Laboratory testing, crucial for medical diagnosis, has 3 phases: preanalytical, analytical, and postanalytical. This study set out to demonstrate whether automating tube labeling through artificial intelligence (AI) support enhances efficiency, reduces errors, and improves outpatient phlebotomy services. The NESLI tube-labeling robot (Labenko Informatics), which uses AI models for tube selection and handling, was used for the experiments. The study evaluated the NESLI robot's operational performance, including labelling time, technical problems, tube handling success, and critical stock alerts. The robot's label readability was also tested on various laboratory devices. This research will contribute to the field's understanding of the potential impact of automated tube-labeling systems on laboratory processes in the preanalytical phase. NESLI demonstrated high performance in labeling processes, achieving a success rate of 99.2% in labeling parameters and a success rate of 100% in other areas. For nonlabeling parameters, the average labeling time per tube was measured at 8.96 seconds, with a 100% success rate in tube handling and critical stock warnings. Technical issues were promptly resolved, affirming the NESLI robot's effectiveness and reliability in automating the tube-labeling processes. Robotic systems using AI, such as NESLI, have the potential to increase process efficiency and reduce errors in the preanalytical phase of laboratory testing. Integration of such systems into comprehensive information systems is crucial for optimizing phlebotomy services and ensuring timely and accurate diagnostics.

AI-guided deep vein thrombosis diagnosis in primary care: protocol for cohort with qualitative assessment.

Deep vein thrombosis (DVT), a formation of blood clots within deep veins, mostly of the proximal lower limb, has an annual incidence of 1-2 per 1,000. Patients who are affected by multiple chronic health conditions and who experience limited mobility are at high risk of developing DVT.Traditional DVT diagnosis involves probabilistic assessment in primary care, followed by specialised ultrasound scans (USS), mainly conducted in hospitals. The emergence of point-of-care ultrasound (POCUS), coupled with artificial intelligence (AI)-applications has the potential to expand primary care diagnostic capabilities. To assess the accuracy and acceptability of AI-guided POCUS for DVT diagnosis when performed by non-specialists in primary care. Diagnostic cross-sectional study coupled with a qualitative evaluation conducted at primary care DVT clinics. First, a diagnostic test accuracy (DTA) study will investigate the accuracy of AI-guided POCUS in 500 individuals with suspected DVT, performed by healthcare assistants (HCAs). The reference standard is the standard of care USS conducted by sonographers. Second, after receiving both scans, participants will be invited to complete a patient satisfaction survey (PSS). Finally, semi-structured interviews with 20 participants and 5 HCAs will explore the acceptability of AI-guided POCUS DVT diagnosis. This study will rigorously evaluate the accuracy and acceptability of AI-guided POCUS DVT diagnosis conducted by non-specialists in primary care.

Dysfunction of the magnocellular subdivision of the visual thalamus in developmental dyslexia.

Developmental dyslexia (DD) is one of the most common learning disorders, affecting millions of children and adults worldwide. To date, scientific research has attempted to explain DD primarily based on pathophysiological alterations in the cerebral cortex. In contrast, several decades ago, pioneering research on five post-mortem human brains suggested that a core characteristic of DD might be morphological alterations in a specific subdivision of the visual thalamus - the magnocellular LGN (M-LGN). However, due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans, this finding was never confirmed in-vivo, and its relevance for DD pathology remained highly controversial. Here, we leveraged recent advances in high-resolution magnetic resonance imaging (MRI) at high field strength (7 Tesla) to investigate the M-LGN in DD in-vivo. Using a case-control design, we acquired data from a large sample of young adults with DD (n = 26; age 28 ± 7 years; 13 females) and matched control participants (n = 28; age 27 ± 6 years; 15 females). Each participant completed a comprehensive diagnostic behavioral test battery and participated in two MRI sessions, including three functional MRI experiments and one structural MRI acquisition. We measured blood-oxygen-level-dependent responses and longitudinal relaxation rates to compare both groups on LGN subdivision function and myelination. Based on previous research, we hypothesized that the M-LGN is altered in DD and that these alterations are associated with a key DD diagnostic score, i.e., rapid letter and number naming (RANln). The results showed aberrant responses of the M-LGN in DD compared to controls, which was reflected in a different functional lateralization of this subdivision between groups. These alterations were associated with RANln performance, specifically in male DD. We also found lateralization differences in the longitudinal relaxation rates of the M-LGN in DD relative to controls. Conversely, the other main subdivision of the LGN, the parvocellular LGN (P-LGN), showed comparable blood-oxygen-level-dependent responses and longitudinal relaxation rates between groups. The present study is the first to unequivocally show that M-LGN alterations are a hallmark of DD, affecting both the function and microstructure of this subdivision. It further provides a first functional interpretation of M-LGN alterations and a basis for a better understanding of sex-specific differences in DD with implications for prospective diagnostic and treatment strategies.

Clinical and Radiological Predictors of Positive Microbiological Yield in Vertebral Osteomyelitis: A Retrospective Cohort Study

Objective: To evaluate demographic, laboratory, biopsy, and imaging variables as potential predictors of microbial identification in patients with suspected vertebral osteomyelitis, aiming to enhance diagnostic accuracy and optimize patient care. Methods: This retrospective cohort study analyzed 83 patients who underwent image-guided percutaneous disc-space sampling between June 2020 and December 2023. Inclusion criteria were adults aged ≥18 years with imaging-based evidence of vertebral osteomyelitis and clinical suspicion of infection. Exclusion criteria were presence of known malignancy and non-infective causes. Demographics, clinical history, imaging, biopsy, and microbiology data were collected. Logistic regression analysis was used to identify predictors of positive microbiological yield. Results: Microorganisms were identified in 32 of 83 cases (38.6%), with Mycobacterium tuberculosis being the most common pathogen identified (21.9%). Elevated C-reactive protein (CRP) levels, erythrocyte sedimentation rate (ESR), and paravertebral signal changes on MRI were associated with a positive culture yield. Multivariate analysis identified CRP as an independent predictor of positive microbiological results. CT-guided percutaneous biopsy was found to be safe, with no major complications reported. Conclusion: CT-guided percutaneous biopsy is a reliable and safe diagnostic tool for identifying the causative pathogens in vertebral osteomyelitis. Elevated CRP levels were independently associated with positive microbiological yield, highlighting its role as a crucial predictor in clinical practice. These findings underscore the importance of incorporating CRP levels into the diagnostic process, potentially guiding the selection of patients for biopsy to improve the detection of infection.

Beyond the AJR: Can Evidence-Based MRI Signs Improve the Diagnostic Accuracy of Idiopathic Intracranial Hypertension?

Beyond the AJR: Can Evidence-Based MRI Signs Improve the Diagnostic Accuracy of Idiopathic Intracranial Hypertension?

Preliminary development and validation of the Indonesian Pediatric Epilepsy Questionnaire (INA-PEPSI) to determine epilepsy and distinguish focal and generalized epilepsy in infants and children with unprovoked seizure in low-resource settings.

To outline the preliminary development and validation of a questionnaire for diagnosing epilepsy and distinguishing focal and generalized epilepsy among infants and children in Indonesia, where electroencephalography and pediatric neurologists are generally not available. A 10-question questionnaire comprising of 43 items was developed through literature review and expert panel discussions. Then, the questionnaire was administered by pediatricians to 75 children aged 1 month to 18 years old presenting with >1 episode of unprovoked seizures at an interval of >24 h. Subsequently, the questionnaire was assessed for content validity with item-level and scale-level content validity indices and ratio, construct validity with item-total correlation tests, criterion validity with diagnostic parameter assessments, and inter-rater reliability using Cohen's kappa (κ) and internal consistency with Cronbach's alpha (α) coefficient. The questionnaire exhibited favorable internal validity and reliability in diagnosing epilepsy and distinguishing focal and generalized epilepsy, with excellent content (both indices and ratio at 1) and construct validity (rcount > rtable at p < 0.001), inter-rater reliability (κ = 0.86 and κ = 0.84), and internal consistency (α = 0.634 and α = 0.806). The questionnaire had a sensitivity and specificity of 96.4% (95%CI 89.1-99.5%) and 95.0% (79.5-99.6%) (area under the curve [AUC] 0.946 [0.900-0.992, p < 0.001]) in diagnosing epilepsy and 80.0% (57.4-95.7%) and 97.4% (89.7-99.2%) (AUC 0.889 [0.783-0.995, p < 0.001]) in distinguishing focal and generalized epilepsy, with a misdiagnosis rate of 4.0%. The questionnaire shows promising potential in diagnosing epilepsy and distinguishing focal and generalized epilepsy. Further external validation studies in larger and more diverse populations are required to confirm our findings. The diagnosis of epilepsy in children is challenging, particularly in resource-limited settings such as Indonesia, where advanced diagnostic tests and pediatric neurologists are scarce. The Indonesian Pediatric Epilepsy Questionnaire (INA-PEPSI) is designed to address these limitations by enabling healthcare professionals in Indonesia to diagnose epilepsy and classify its types without relying on advanced diagnostic tools. Although the questionnaire is still in the early stages of development and validation, this study demonstrates that the questionnaire exhibits good overall diagnostic performance in diagnosing epilepsy and distinguishing epilepsy types among Indonesian children.

Willingness to participate in a personalized health cohort – insights from the swiss health study pilot phase

BackgroundThis paper explores the feasibility of establishing a large-scale population-based cohort and biobank in Switzerland by assessing potential participants’ needs, expectations, and concerns about such an infrastructure providing information on health, lifestyle, and exposure trajectories, the development of disease, and risk factors over time.MethodsWe utilized a scenario-based questionnaire in the Swiss Health Study pilot phase (2020–2021), involving 1349 adults aged 20–69 from the cantons Vaud and Bern. We conducted descriptive statistics supported by R and qualitative content analysis of n = 374 open responses related to attitudes towards research.ResultsWe highlight the benefits and challenges of the scenario-based approach, discuss the sample represented in the pilot phase, and present implications for building a full cohort. We also report on participants’ attitudes towards and previous experience with health research. We analyze references regarding informed consent and feedback, attitudes towards the Swiss Health Study, and recommendations on improving its scope, design, and instruments. Results indicate a high interest (90%) in participating in a national health study, with 85% of a random population sample willing to join a long-term cohort. Only 43% were familiar with biobanks, and 44% preferred general consent. Trust was high for Swiss-based public research but lower for researchers from other countries or private sector. Over 95% expressed willingness to complete online questionnaires, undergo physical examination, and donate biosamples. Almost all participants wanted to know the outcomes of the medical tests (99.5%) and the exposure to environmental stressors (95%) from their study center visit. Preferred tools for monitoring sleep, physical activity, and diet were known smartphone apps with automatic data management.ConclusionOverall, the study reveals a positive attitude towards personalized health research, with a strong willingness to share data and samples. Key insights focus the meaning of informed consent for participation, the relevance of sampling and representativeness, as well as the significance and challenges of personalized feedback, especially regarding environmental health concerns. Findings emphasize participants’ supportive yet reflexive stances, underscoring the importance of aligning research values with individual values in personalized health research. These insights contribute valuable considerations for refining the scope, design, and instruments of future cohort studies.

Imaging of Carotid Stenosis: Where Are We Standing? Comparison of Multiparametric Ultrasound, CT Angiography, and MRI Angiography, with Recent Developments

Atherosclerotic disease of the carotid arteries is a crucial risk factor in predicting the likelihood of future stroke events. In addition, emerging studies suggest that carotid stenosis may also be an indicator of plaque load on coronary arteries and thus have a correlation with the risk of acute cardiovascular events. Furthermore, although in symptomatic patients the degree of stenosis is the main morphological parameter studied, recent evidence suggests, especially in asymptomatic patients, that plaque vulnerability should also be evaluated as an emerging and significant imaging parameter. The reference diagnostic methods for the evaluation of carotid stenosis are currently ultrasonography, magnetic resonance imaging (MRI), and computed tomography angiography (CTA). In addition, other more invasive methods such as 123I-metaiodobenzylguanidine (MIBG) scintigraphy and PET-CT, as well as digital subtraction angiography, can be used. Each method has advantages and disadvantages, and there is often some confusion in their use. For example, the usefulness of MRI is often underestimated. In addition, implementations for each method have been developed over the years and are already enabling a significant increase in diagnostic accuracy. The purpose of our study is to make an in-depth analysis of all the methods in use and in particular their role in the diagnostic procedure of carotid stenosis, also discussing new technologies.

Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN

To solve the problem that the existing planetary gear train fault diagnosis methods have, namely their low diagnostic accuracy under low signal-to-noise ratio (SNR), a fault diagnosis method based on a double channel-deep residual shrinkage network (DC-DRSN) is proposed. The short-time Fourier transform (STFT) is used to convert the original vibration signal into a two-dimensional time-frequency graph, which effectively enhances the ability to express information. A DC-DRSN model is constructed, and the optimal number of residual shrinkage modules is determined by combining the diagnostic characteristics with different noises, which effectively improves the accuracy and anti-noise ability of fault diagnosis. The results of bearing and planetary gear train crack fault diagnosis show that the diagnosis method based on DC-DRSN has higher diagnostic accuracy while realizing fault diagnosis, which is better than other deep learning diagnosis methods. At the same time, the method can adapt to fault diagnosis in different noise environments, and has good expression ability and generalization ability.