Diagnostic Accuracy Research Articles

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder (MDD). However, few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity (FC). AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents. METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study. Using resting-state functional magnetic resonance imaging, the FC was compared between the adolescents with MDD and the healthy controls, with the bilateral amygdala serving as the seed point, followed by statistical analysis of the results. The support vector machine (SVM) method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD. RESULTS Compared to the controls and using the bilateral amygdala as the region of interest, patients with MDD showed significantly lower FC values in the left inferior temporal gyrus, bilateral calcarine, right lingual gyrus, and left superior occipital gyrus. However, there was an increase in the FC value in Vermis-10. The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls, achieving a diagnostic accuracy of 83.91%, sensitivity of 79.55%, specificity of 88.37%, and an area under the curve of 67.65%. CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

Technical feasibility of automated blur detection in digital mammography using convolutional neural network

Abstract Background The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mammography. Methods A retrospective dataset consisting of 152 examinations acquired with mammography machines from three different vendors was utilized. The blurred areas were contoured by expert breast radiologists. Normalized Wiener spectra (nWS) were extracted in a sliding window manner from each mammogram. These spectra served as input for a convolutional neural network (CNN) generating the probability of the spectra originating from a blurred region. The resulting blur probability mask, upon thresholding, facilitated the classification of a mammogram as either blurred or sharp. Ground truth for the test set was defined by the consensus of two radiologists. Results A significant correlation between the view (p < 0.001), as well as between the laterality and the presence of blur (p = 0.004) was identified. The developed model AUROC of 0.808 (95% confidence interval 0.794–0.821) aligned with the consensus in 78% (67–83%) of mammograms classified as blurred. For mammograms classified by consensus as sharp, the model achieved agreement in 75% (67–83%) of them. Conclusion A model for blur detection was developed and assessed. The results indicate that a robust approach to blur detection, based on feature extraction in frequency space, tailored to radiologist expertise regarding clinical relevance, could eliminate the subjectivity associated with the visual assessment. Relevance statement This blur detection model, if implemented in clinical practice, could provide instantaneous feedback to technicians, allowing for prompt mammogram retakes and ensuring that only high-quality mammograms are sent for screening and diagnostic tasks. Key Points Blurring in mammography limits radiologist interpretation and diagnostic accuracy. This objective blur detection tool ensures image quality, and reduces retakes and unnecessary exposures. Wiener spectrum analysis and CNN enabled automated blur detection in mammography. Graphical Abstract

Diagnostic performance of dual-energy CT with electron-density reconstruction for lumbar disc herniation

Background: Magnetic resonance imaging (MRI) is used for the evaluation of degenerative spinal disease. However, its utility is restricted in routine practice because of contraindications and a lack of widespread availability. Dual-energy computed tomography (DECT) is a newer technique for the evaluation of degenerative spinal disease.Objectives: This study aimed to evaluate the diagnostic performance of DECT with electron-density (ED) image reconstruction compared to standard CT for the detection of lumbar disc herniation, with MRI as the gold standard.Method: The retrospective study included 84 patients between 01 July 2023 to 31 December 2023 who underwent DECT and 1.5-T MRI within 1 week. Four radiologists, blinded to the clinical and MRI information, independently evaluated the standard CT series and DECT series with ED reconstructions for lumbar disc herniation and spinal nerve root impingement. The gold standard for comparison was lumbar spine MRI, and diagnostic accuracy was measured with sensitivity and specificity.Results: MRI revealed 417 lumbar disc herniations. Dual-energy computed tomography with ED reconstruction showed higher sensitivity (86.36% [532/616] vs. 57.79% [356/616]) and specificity (96.86% [1019/1052] vs. 95.82% [1008/1052]) for the detection of lumbar disc herniation compared to standard CT.Conclusion: Dual-energy computed tomography with ED reconstruction shows better diagnostic performance for the detection of lumbar disc herniation compared to standard CT and can be a useful alternative imaging modality when MRI is contraindicated or unavailable.Contribution: This study shows the usefulness of DECT as an alternative imaging technique for screening of degenerative spinal disease whenever MRI is contraindicated or unavailable.

It’s More Complicated Than Interpreter Use: Improving Care for Non–English-Speaking Families

Mateo is a 2-week-old male who presented for evaluation of neonatal fever and whose parents speak Spanish. Workup revealed pyelonephritis, underlying renal anomalies, and vesicoureteral reflux. A complex interplay of individual and contextual factors contributed to Mateo not receiving prophylactic antibiotics, which ultimately led to a potentially preventable subsequent admission for sepsis secondary to Escherichia coli pyelonephritis with bacteremia. Further history revealed additional communication breakdown; despite multiple appointments between hospitalizations, systems issues led to Mateo’s primary care provider being unaware of Mateo’s admissions or urologic diagnoses. This case demonstrates challenges facing health care providers as we strive to provide equitable care to families speaking languages other than English (LOE). Although an in-person interpreter was used throughout Mateo’s hospitalization, there were still key areas of communication breakdown and opportunities for improvement. First, we describe characteristics of the growing population speaking LOE in the United States and discuss qualitative and quantitative disparities in health care encounters for this population. Through Mateo’s experience, we highlight the transition of care at discharge as being highly vulnerable to communication breakdown, particularly for patients speaking LOE, and emphasize the additional impact of low health literacy on these challenges. Finally, we outline potential strategies to reduce disparities and improve outcomes for pediatric patients and families speaking LOE, focusing on (1) consistent and evidence-based interpreter use, (2) incorporation of health literacy–informed communication strategies in the discharge process, (3) reducing barriers to follow-up appointments or diagnostic testing, and (4) closed-loop communication with the patient’s primary care provider.

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning

AbstractLeukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical diagnostic and typing tests are complex and time‐consuming. In particular, all of these tests rely on bone marrow aspiration, which is invasive and leads to poor patient compliance, exacerbating treatment delays. Morphological analysis of peripheral blood cells (PBC) is still primarily used to distinguish between benign and malignant hematologic disorders, but it remains a challenge to diagnose and type these diseases solely by direct observation of peripheral blood(PB) smears by human experts. In this study, we apply a segmentation‐based enhanced residual network that uses progressive multigranularity training with jigsaw patches. It is trained on a self‐built annotated dataset of 21,208 images from 237 patients, including five types of benign white blood cells(WBCs) and eight types of leukemic cells. The network is not only able to discriminate between benign and malignant cells, but also to typify leukemia using a single peripheral blood cell. The network effectively differentiated acute promyelocytic leukemia (APL) from other types of acute myeloid leukemia (non‐APL), achieving a precision rate of 89.34%, a recall rate of 97.37%, and an F1 score of 93.18% for APL. In contrast, for non‐APL cases, the model achieved a precision rate of 92.86%, but a recall rate of 74.63% and an F1 score of 82.75%. In addition, the model discriminates acute lymphoblastic leukemia(ALL) with the Ph chromosome from those without. This approach could improve patient compliance and enable faster and more accurate typing of leukemias for early diagnosis and treatment to improve survival.

Multiphase CTA Collateral Score to Identify Intracranial Atherosclerotic Stenosis-Related Large Vessel Occlusion

Objective: Identification of acute ischemic stroke with large vessel occlusion (AIS-LVO) etiology is crucial for effective revascularization therapy. As collaterals are pivotal in maintaining cerebral perfusion in intracranial atherosclerotic stenosis (ICAS), we investigated whether multiphase CT angiography (mCTA) collateral score can be a diagnosis marker of ICAS-related LVO. Methods: We reviewed clinical and imaging data from 92 patients who presented with AIS-LVO and underwent mCTA (57 ICAS-related LVO and 35 embolic LVO). Logistic regression was used to identify ICAS-related LVO. The diagnostic accuracy of the mCTA collateral score for identifying ICAS-related LVO was determined using receiver operating characteristic (ROC) analysis. Results: Compared with patients with embolic LVO, those with ICAS-related LVO had a high median mCTA collateral score (4 vs. 3; P<0.0001). The multinomial logistic regression analysis revealed a significant increase in the mCTA collateral score (OR: 3.717, 95% CI: 2.009-6.876, P<0.0001) in patients with ICAS-related LVO. ROC analysis revealed that the optimal cutoff point of the mCTA collateral score to diagnosis the ICAS-related LVO was 3.5, the area under the curve (AUC) was 0.817 (95% CI: 0.736-0.899; P<0.0001), sensitivity was 80.7%, and specificity was 74.3%. Further analysis revealed that patients with a 4 to 5 mCTA collateral score exhibited a significantly higher median modified Rankin Scale (mRS) at discharge compared with those with a 0 to 3 score (P=0.0464). Conclusions: The mCTA collateral score may be associated with ICAS-related LVO and could be beneficial in identifying the etiology of AIS-LVO.

Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study

Purpose: Elastography increased the diagnostic accuracy of liver fibrosis. However, several challenges persist, including the widespread utilization of equipment, difficulties in measuring certain cases, and the influence of viscosity factors. A rough surface and a blunted hepatic margin have long been acknowledged as valuable characteristics indicative of hepatic fibrosis. The objective of this study was to conduct an image analysis and quantitative assessment of the contour of the sagittal section of the left lobe of the liver. Methods: Between February and October 2020, 486 consecutive outpatients underwent ultrasound examinations at our hospital. A total of 214 images were manually annotated by delineating the liver contour to create annotation images. U-Net was employed for liver segmentation, with the dataset divided into training (n = 128), testing (n = 42), and validation (n = 44) subsets. Additionally, 43 Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) cases with pathology data from between 2015 and 2020 were included. Segmentation was performed using the program developed in the first step. Subsequently, shape analysis was conducted using ImageJ. Results: Liver segmentation exhibited high accuracy, as indicated by Dice loss of 0.044, Intersection over Union of 0.935, and an F score of 0.966. The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. Image analysis showed MinFeret and Minor correlated with liver fibrosis stage (p = 0.046, 0.036, respectively). Sensitivity, specificity, and AUROC of Minor for ≥F3 were 0.571, 0.862, and 0.722, respectively, and F4 were 1, 0.600, and 0.825, respectively. Conclusion: Deep learning segmentation of the sagittal cross-sectional contour of the left lobe of the liver demonstrated commendable accuracy. The roughness of the liver surface was correctly judged by artificial intelligence. Image analysis showed the thickness of the left lobe inversely correlated with liver fibrosis stage.

Diagnostic Performance of Artificial Intelligence in Chest Radiographs Referred from the Emergency Department

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice.

Assessing coronary artery stenosis exacerbated impact on left ventricular function and deformation in metabolic syndrome patients by 3.0 T cardiac magnetic resonance imaging

Abstract Background Metabolic syndrome (MetS) and coronary artery stenosis (CAS) independently increase the risk of cardiovascular events, while the impact of CAS on left ventricular (LV) function and deformation in MetS patients remains unclear. This study investigates how varying degrees of CAS exacerbate LV function and myocardial deformation in MetS patients. Methods One hundred thirty-one MetS patients who underwent CMR examinations were divided into two groups: the MetS(CAS−) group (n = 47) and the MetS(CAS+) group (n = 84). The MetS(CAS+) group was divided into MetS with non-obstructive CAS(NOCAS+) (n = 30) and MetS with obstructive CAS(OCAS+) group (n = 54). Additionally, 48 age- and sex-matched subjects were included as a control group. LV functional and deformation parameters were measured and compared among subgroups. The determinants of decreased LV global peak strains in all MetS patients were identified using linear regression. The receiver operating characteristic (ROC) curve and logistic regression model (LRM) evaluated the diagnostic accuracy of the degree of CAS for identifying impaired LV strain. Results Compared to MetS(CAS−), MetS(NOCAS+) showed a significantly increased LV mass index (p < 0.05). Global longitudinal peak strain was decreased gradually from MetS(CAS−) through MetS(NOCAS+) to MetS(OCAS+) (− 13.02 ± 2.32% vs. − 10.34 ± 4.05% vs. − 7.55 ± 4.48%, p < 0.05). MetS(OCAS+) groups showed significantly decreased LV global peak strain (GPS), PSSR and PDSR in radial and circumferential directions compared with MetS(NOCAS+) (all p < 0.05). The degree of CAS was independently associated with impaired global radial peak strain (GRPS) (β = − 0.289, p < 0.001) and global longitudinal peak strain (GLPS) (β = 0.254, p = 0.004) in MetS patients. The ROC analysis showed that the degree of CAS can predict impaired GRPS (AUC = 0.730) and impaired GLPS (AUC = 0.685). Conclusion Besides traditional biochemical indicators, incorporating CAS assessment and CMR assessment of the LV into routine evaluations ensures a more holistic approach to managing MetS patients. Timely intervention of CAS is crucial for improving cardiovascular outcomes in this high-risk population.

Unprecedented Co-occurrence: Identification of a Pathogenic Genetic Variant in the KMT2B Gene in a Wilson Disease Patient with a Pathogenic ATP7B Mutation

The pathophysiology of dystonia in Wilson disease (WD) is complex and poorly understood. Copper accumulation in the basal ganglia, disrupts dopaminergic pathways, contributing to dystonia’s development via neurotransmitter imbalance. Despite advances in diagnosis and management, WD with dystonia remains a challenging condition to treat. We aim to report the unprecedented co-occurrence of pathogenic genetic variants in both the ATP7B and KMT2B genes in a patient with WD. A 13-year-old male presented at 12 with dysarthria and bilateral Kayser-Fleischer rings. Over months, dystonia spread to his left foot, upper limb, and trunk, accompanied by slowed daily activities. Diagnostic tests included MRI for brain structure, abdominal ultrasound for liver function, serum ceruloplasmin and copper levels to assess copper metabolism, and 24-hour urine copper tests for excretion levels. Whole exome sequencing was conducted using genomic DNA from peripheral blood samples. Variant classification followed guidelines from the American College of Medical Genetics and Genomics. The sequencing revealed compound heterozygous pathogenic variants in the ATP7B gene: NM_000053.4:c.2165dupT and NM_000053.4:c.813C>A. A pathogenic variant in the KMT2B gene, NM_014727:c.3052delA, was identified. This case highlights WD co-occurrence with ATP7B and KMT2B mutations, suggesting KMT2B as a potential genetic modifier.

Progressive Bulbar Palsy Care: Exploring the Potential of Functional MRI for Early Detection

Progressive bulbar palsy (PBP) is a neurodegenerative disorder impacting motor neurons involved in speech, swallowing, and breathing. Early diagnosis is challenging due to limited diagnostic tools. Functional MRI (fMRI) holds promise for detecting early biomarkers and tracking disease progression, potentially improving clinical management. This study aims to explore the utility of fMRI in developing imaging biomarkers for the early detection, prognostication, and monitoring of PBP. By addressing existing gaps in the literature, the research seeks to demonstrate how fMRI can enhance diagnostic accuracy, provide predictive insights into disease progression, and improve patient outcomes. A systematic literature review was conducted using databases such as Google Scholar. The search focused on articles published from 2018 onwards, using keywords related to PBP and fMRI. Inclusion criteria included peer-reviewed studies involving human subjects relevant to the use of fMRI in PBP. Exclusion criteria excluded non-peer-reviewed articles, non-English publications, and studies not specifically addressing fMRI in PBP. Data synthesis involved both qualitative and quantitative analyses of identified biomarkers, diagnostic accuracy, and prognostic value. The review identified several fMRI biomarkers that show potential for early detection and monitoring of PBP. These biomarkers, derived from functional and structural changes in corticobulbar pathways, offer enhanced sensitivity compared to traditional diagnostic methods. Predictive models based on fMRI metrics can forecast disease progression, aiding in personalized treatment planning. The study concludes that integrating fMRI into clinical practice could lead to earlier interventions, more accurate prognostic assessments, and improved management of PBP, ultimately enhancing patient quality of life and outcomes.

Artificial Intelligence in Emergency Medicine: A Literature Review

IntroductionArtificial intelligence (AI) is rapidly transforming medical fields, particularly emergency medicine (EM), where timely decision-making is crucial. AI offers potential benefits in diagnostic accuracy, patient care optimization, and workflow efficiency within emergency departments (EDs). Purpose of WorkThis review aims to synthesize recent advancements in AI applications within emergency medicine, focusing on diagnostic support, patient triage, clinical decision support systems (CDSS), and workflow optimization. Additionally, we highlight the potential benefits, challenges, and future directions for AI in EM. Material and MethodsA comprehensive literature search was conducted using PubMed and Google Scholar databases. We reviewed peer-reviewed articles from 2008 to 2024, focusing on AI-driven solutions in EDs. Keywords included "artificial intelligence," "emergency medicine," "machine learning," and "clinical decision support." Studies were selected based on their relevance to AI applications in EM, diagnostic improvements, and operational efficiency. The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload. However, challenges like ethical concerns, data bias, and the need for clinical validation remain. Further research is necessary to integrate AI more effectively in clinical practice.

A Study on Alzhemier Disease in Takum, Taraba State, Nigeria: An ARIMA Model Approach

Alzheimer’s disease, a progressive neurodegenerative disorder, represents a significant and growing public health burden, particularly as life expectancy increases worldwide. In sub-Saharan Africa, including Nigeria, the disease's prevalence is rising due to aging populations and urbanized lifestyles that elevate risk factors for cognitive decline. This study investigates the trends and projected incidence of Alzheimer’s disease in Takum, Taraba State, Nigeria. The research utilizes patient records from General Hospital Takum, spanning from 2012 to 2021. Following diagnostic tests and data transformations, the ARIMA(1,2,0) model was selected as the best fit for predicting future case counts. The findings reveal a steady increase in Alzheimer’s cases, consistent with global patterns, highlighting the need for proactive measures in healthcare planning. The study thus recommends the need for increased public awareness, investment in diagnostic infrastructure, and support systems for caregivers.

Point-of-Care Diagnostics Using Self-heating Elements from Smart Food Packaging: Moving Towards Instrument-Free Nucleic Acid-Based Detection.

Compromising between accuracy and rapidity is an important issue in analytics and diagnostics, often preventing timely and appropriate reactions to disease. This issue is particularly critical for infectious diseases, where reliable and rapid diagnosis is crucial for effective treatment and easier containment, thereby reducing economic and societal impacts. Diagnostic technologies are vital in disease modeling, tracking, treatment decision making, and epidemic containment. At the point-of-care level in modern healthcare, accurate diagnostics, especially those involving genetic-level analysis and nucleic acid amplification techniques, are still needed. However, implementing these techniques in remote or non-laboratory settings poses challenges because of the need for trained personnel and specialized equipment, as all nucleic acid-based diagnostic techniques, such as polymerase chain reaction and isothermal nucleic acid amplification, require temperature cycling or elevated and stabilized temperatures. However, in smart food packaging, there are approved and commercially available methods that use temperature regulation to enable autonomous heat generation without external sources, such as chemical heaters with phase change materials. These approaches could be applied in diagnostics, facilitating point-of-care, electricity-free molecular diagnostics, especially with nucleic acid-based detection methods such as isothermal nucleic acid amplification. In this review, we explore the potential interplay between self-heating elements, isothermal nucleic acid amplification techniques, and phase change materials. This paves the way for the development of truly portable, electricity-free, point-of-care diagnostic tools, particularly advantageous for on-site detection in resource-limited remote settings and for home use.

Fault Diagnosis of Aircraft Hydraulic Pipeline Clamps Based on Improved KPCA and WOA–KELM

Due to the complexity and diversity of aviation hydraulic pipeline systems, there has been a lack of qualitative formulas or characteristic indicators to describe clamp failures within these systems. In this paper, based on the data-driven idea, an improved KPCA-based feature extraction method is proposed and combined with the optimized KELM for fault diagnosis and condition monitoring of aviation hydraulic line clamps. Firstly, the kernel parameters of KPCA are combined using polynomial and Gaussian kernels based on their proportional weights. Secondly, a GA–PSO (Genetic Algorithm–Particle Swarm Optimization) hybrid algorithm is employed to optimize the kernel parameters, selecting 13 time-domain and 4 frequency-domain feature indicators to form the initial feature dataset, which is then subjected to dimensionality reduction using the improved KPCA. Finally, diagnosis is conducted using a KELM optimized by the whale optimization algorithm. The results indicate that, across multiple diagnostic trials, the average diagnostic accuracy can reach 99.99%, providing a feasible approach for the precise diagnosis of clamp faults in aviation hydraulic pipeline systems.

Effects on lymph node size, staging and primary tumor histology on diagnostic accuracy of axillary lymph node aspirate of breast cancers.

Fine-needle aspiration cytology is preferred for axillary lymph node metastasis with low costs and minimal risks. To improve diagnostic performance by incorporating clinical-radiological-pathological parameters, a large cohort pre-operative aspirates in were reviewed for parameters affecting adequacy rate and accuracy. Axillary nodal aspirates from three institutions with histologic correlation were retrieved. Case notes were reviewed for parameters pertaining to the primary tumor, nodal status, histologic and cytologic diagnoses. Totally 1361 specimens were included. The risk of malignancy for C1-C5 categories were 53.39%, 27.45%, 70.97%, 83.33% and 88.00%, increasing to 75.86%, 94.59% and 99.28% for C3/C4/C5 categories excluding cases with neoadjuvant therapy. Node size (p < 0.001) and histologic grade (p = 0.003) of primary tumor positively correlated with specimen adequacy. Presence of in situ component trended towards inadequacy (p = 0.069). Lymph node size remained a strong predictor of concordant cytologic diagnosis (p < 0.001). A higher percentage of involved node (p = 0.006) and HER2 overexpressed breast cancers (p = 0.027) increased concordance. Cases with ≥ 4 (up to ≥ 10) positive nodes were more likely to be concordant (p = 0.009- < 0.001), with improvements of 8.27%-12.37%. For size, cut-offs of ≥ 5 and ≥ 10mm were significant (p = 0.006- < 0.001). It is critical that clinical-radiological-pathological findings be interpreted together with cytology. Aspirates from smaller nodes are more likely to be non-informative, irrespective of the total number of suspicious nodes, or a high-grade primary. In axillae with less than 4 suspicious nodes and/or a target node of less than 5-10mm, the diagnostic accuracy of aspiration cytology decreases and should be interpreted cautiously.

Application of artificial intelligence in VSD prenatal diagnosis from fetal heart ultrasound images.

Developing a combined artificial intelligence (AI) and ultrasound imaging to provide an accurate, objective, and efficient adjunctive diagnostic approach for fetal heart ventricular septal defects (VSD). 1,451 fetal heart ultrasound images from 500 pregnant women were comprehensively analyzed between January 2016 and June 2022. The fetal heart region was manually labeled and the presence of VSD was discriminated by experts. The principle of five-fold cross-validation was followed in the training set to develop the AI model to assist in the diagnosis of VSD. The model was evaluated in the test set using metrics such as mAP@0.5, precision, recall, and F1 score. The diagnostic accuracy and inference time were also compared with junior doctors, intermediate doctors, and senior doctors. The mAP@0.5, precision, recall, and F1 scores for the AI model diagnosis of VSD were 0.926, 0.879, 0.873, and 0.88, respectively. The accuracy of junior doctors and intermediate doctors improved by 6.7% and 2.8%, respectively, with the assistance of this system. This study reports an AI-assisted diagnostic method for VSD that has a high agreement with manual recognition. It also has a low number of parameters and computational complexity, which can also improve the diagnostic accuracy and speed of some physicians for VSD.

Towards Carbon Neutrality: The Impact of Private AI Investment and Financial Development in the United States – An Empirical Study Using the STIRPAT Model

This investigation analyses the influence of private AI investment and financial development (FD) on CO2 emissions in the United States, using the STIRPAT framework to account for the functions of GDP, population, and foreign direct investment (FDI). The data's robustness was verified through the application of a variety of unit root tests, which confirmed that the variables are free of unit root issues and exhibit a varied order of integration. The ARDL bound test was used to investigate the cointegration among the variables and it found a long-run equilibrium relationship. The ARDL model results show that income, FDI, FD, and population significantly increase CO2 emissions in both the short and long term. In contrast, we found that private investment in AI led to a significant reduction in CO2 emissions over these time frames. Additional estimations were conducted using FMOLS, DOLS, and CCR methods to verify the ARDL results, all of which attested to the initial findings' robustness. In addition, the study implemented a pairwise Granger causality test to illustrate the directional relationships between the variables. There is a unidirectional causal link between GDP, private AI investment, FDI, population, and CO2 emissions, according to the findings. Most notably, we observed bidirectional causality between CO2 emissions and FD. Diagnostic tests further corroborated the validity of the study's conclusions, confirming that the model is free from specification errors, serial correlation, and heteroscedasticity.

Deciphering the Role of Androgen in the Dermatologic Manifestations of Polycystic Ovary Syndrome Patients: A State-of-the-Art Review

Polycystic ovary syndrome is a presvalent endocrine disorder affecting women globally, characterized by hormonal imbalance, metabolic disturbances, and reproductive dysfunction. Diagnosis relies on clinical evaluation, medical history assessment, physical examination, and laboratory tests, with the Rotterdam criteria being widely used. The pathophysiology of PCOS involves genetic predisposition, environmental factors, and lifestyle influences, with hormonal dysregulation, particularly elevated androgens, insulin resistance, and chronic inflammation, playing a pivotal role. These mechanisms not only contribute to reproductive and metabolic disturbances but also to the various cutaneous manifestations, such as acne, hirsutism, and alopecia. This review aims to analyze the role of androgens in the dermatological manifestations in patients with polycystic ovary syndrome, providing insights into underlying mechanisms and guiding the development of effective therapeutic strategies. By synthesizing available evidence, this review aims to deepen understanding of related dermatological manifestations and improve outcomes for affected individuals.

Validity of Mehrviru® and Sacace® in molecular detection of HPV: a latent class analysis.

Valid diagnostic tests for human papillomavirus (HPV) detection are crucial to identify individuals at high risk of cervical cancer. We assessed and compared the validity of Mehrviru HPV genotyping and Sacace (HPV Genotypes 14 Real-TM Quant) for molecular detection of 14 high-risk human papillomaviruses. We used three HPV test results to identify HPV-positive individuals (14 high-risk genotypes) in a specialist gynecology clinic. The HPV test results were collected using Mehrviru®, Sacace®, and a third kit from the clinical diagnostic laboratory. We used Latent class analysis to determine the actual status of HPV infection in study participants. The sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, Youden, and area under the ROC curve indices of each diagnostic kit and their 95% confidence intervals were calculated. The agreement between the Mehrviru and Sacace kits was determined using the Kappa statistic. We examined 117 women at high risk of HPV infection. The mean age (SD) was 37.2 (9.1). According to LCA, 28.6% of participants had an HPV infection. The sensitivity and specificity (95% CI) of the Mehrviru were 90.8% (73.7-97.2%) and 90.9% (82.1-95.6%), and corresponding figures for Sacace were 92.0% (72.3-98.1%) and 97.4% (90.2-99.3%). The kappa index between the Mehrviru and Sacace kits was 69.7% (55.6-83.9%). The area under the ROC curve for Mehrviru and Sacace test were 91.4% (85.7-97.1%) and 94.4% (89.3-99.5%). There is an excellent agreement between Mehrviru and Sacace test results, and the diagnostic accuracy indices were similar.