Diagnostic Precision Research Articles

Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders.

Schizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision.

Expert opinion on current issues and challenges in irritable bowel syndrome

Irritable bowel syndrome (IBS) is a widely prevalent functional gastrointestinal disorder characterized by symptoms such as abdominal pain, irregular bowel movements, and bloating, which significantly impact affected individuals' well-being. The increasing prevalence of IBS in India places a substantial burden on clinical practice. Although diagnostic criteria, such as the commonly used Rome IV criteria, have shown reasonable sensitivity, the diagnosis of IBS remains a nuanced and culturally influenced process. This comprehensive review systematically examines current challenges surrounding IBS prevalence, diagnostic intricacies, and the complex pathophysiology involving visceral hypersensitivity. The review highlights the imperative for noninvasive biomarkers to augment diagnostic precision and emphasizes on following a holistic approach to IBS management as advocated by Indian guidelines. This approach integrates dietary modifications, antispasmodic medications, psychological therapy, and yoga, with specific emphasis on tailoring treatments to individual IBS subtypes. Among antispasmodic drugs, pinaverium bromide because of its unique mechanism of action as a calcium channel antagonist within the gastrointestinal tract has shown promise in alleviating IBS symptoms.

Vestibular Migraine and Recurrent Vertigo in Children: A Diagnostic Focus From a Tertiary Hospital Study

BackgroundTo improve diagnostic precision in pediatric vertigo, particularly in Vestibular Migraine of Childhood (VMC), probable VMC (pVMC), Recurrent Vertigo of Childhood (RVC), and unspecified categories, by delineating clinical characteristics and prevalence to refine diagnostics and treatments. MethodsRetrospective analysis of 102 pediatric patients (five to 18 years; 46 females, 56 males) at the Dizziness Center of the Otolaryngology Department in a tertiary-level hospital from January 2019 to December 2023. Patients were classified into VMC, pVMC, RVC, and indeterminate groups. Evaluations included audiometry and vestibular tests (video head impulse test [vHIT] or caloric testing), conducted in the audiology unit and vestibular testing laboratory. Data were analyzed using IBM SPSS 20.0. ResultsDiagnoses were 8.8% VMC, 31.4% pVMC, 51.0% RVC, and 8.8% indeterminate. Nausea and vomiting were common in VMC and pVMC; cochlear symptoms like tinnitus and hearing loss predominated in VMC. Although vestibular testing showed no significant group differences, VMC had more vHIT abnormalities and RVC had more caloric test anomalies. ConclusionsThis study highlights the need for comprehensive diagnostics in pediatric vestibular disorders, revealing unique and overlapping traits across VMC, pVMC, and RVC. Insights call for further research to refine diagnostic criteria and improve treatment methods.

Hystero-Salphingography in current clinical practice-old flames, die hard!

Hysterosalpingography (HSG) remains a valuable diagnostic tool in current clinical practice, offering crucial insights into endometrial cavity, fallopian tubes and the adjoining part of the pelvic peritoneal cavity. Despite the emergence of alternative imaging and non-imaging options, HSG continues to be widely utilized due to its diagnostic accuracy, cost-effectiveness, and easy accessibility. Due attention to the correct technique and optimal image interpretation will further enhance its diagnostic accuracy and precision in the work-up of patients with fertility problems.

Advances in Vascular Imaging: A Comparative Analysis of Doppler Ultrasound and Multidetector CT for Lower Limb Peripheral Arterial Disease Diagnosis.

Background This study explores the comparison between Doppler ultrasound and multidetector CT angiography (MDCTA) in diagnosing peripheral arterial disease (PAD), emphasizing the urgent need for precise and minimally invasive methodologies in vascular medicine. PAD, stemming from atherosclerosis, manifests as reduced blood flow and symptoms, such as claudication, requiring timely and accurate diagnosis for optimal treatment outcomes. Doppler ultrasound emerges as an option, offering a non-invasive and cost-effective approach. Conversely, MDCTA provides intricate images, albeit with associated risks, such as radiation exposure and potential complications from contrast agents. This research rigorously evaluates the efficacy, safety, and cost-efficiency of these modalities, aiming to provide clinicians with valuable insights for informed decision-making, ultimately enhancing standards of patient care. Methodology In this prospective study conducted at Saveetha Medical College, Chennai, 34 patients diagnosed with PAD were enrolled to compare the efficacy of duplex ultrasound and MDCTA in identifying arterial lesions. Statistical analysis comprised kappa statistics and contingency tables to evaluate the concordance between the modalities, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) being calculated. Exclusions were made for patients with contraindications to MDCTA, those under 18 years of age, severe renal impairment, and allergies to contrast agents. This research examined the diagnostic accuracy of both imaging techniques, aiming to provide valuable insights into their effectiveness in identifying arterial lesions associated with PAD. Statistical analysis This investigation studied the efficacy of Doppler ultrasound and MDCTA in diagnosing PAD, with a particular focus on comparing the accuracy of Doppler ultrasonography (DUS) against MDCTA using sensitivity, specificity, and Cohen's kappa coefficient. Through segmental analysis, valuable insights were garnered into the diagnostic precision of DUS across various arterial segments. The results underscored the significance of DUS as a safe, cost-effective, and non-invasive alternative that complements the utility of MDCTA. This comprehensive assessment sheds light on the comparative strengths of both modalities, offering invaluable guidance for clinicians in selecting optimal diagnostic approaches for PAD assessment. Statistical analysis was conducted using the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 24.0, Armonk, NY). Results The sensitivity of ultrasonography (USG) arterial Doppler in evaluating the supra-inguinal, femoropopliteal segments, and infrapopliteal segments was 87.5%, 100%, and 75.32%, respectively. The specificity in evaluating supra-inguinal, femoropopliteal segments, and infrapopliteal segments was 100%, 96.01%, and 83.06%, respectively. The agreement between the two modalities (USG arterial Doppler and CT angiography) obtained by Cohen's kappa analysis with respect to the aortoiliac region and femoropopliteal region was very good (0.91). For infrapopliteal vessels, it was only moderate (0.76). Conclusion Duplex ultrasound emerges as an indispensable tool in the investigation of PAD, offering safety, affordability, and non-invasiveness alongside high diagnostic accuracy and substantial concordance with MDCTA.

Assessment of Bone Age Based on Hand Radiographs Using Regression-Based Multi-Modal Deep Learning.

(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach enhances the model's robustness and diagnostic precision, addressing challenges related to imbalanced data distribution and limited sample sizes. (3) Results: The model exhibited good performance on BAA, with an overall mean absolute error (MAE) of 0.410, root mean square error (RMSE) of 0.637, and accuracy of 91.1%. Subgroup analysis revealed higher accuracy in females ≤ 11 years (MAE: 0.267, RMSE: 0.453, accuracy: 95.0%) and >11 years (MAE: 0.402, RMSE: 0.634, accuracy 92.4%) compared to males ≤ 13 years (MAE: 0.665, RMSE: 0.912, accuracy: 79.7%) and >13 years (MAE: 0.647, RMSE: 1.302, accuracy: 84.6%). (4) Conclusion: This model showed a generally good performance on BAA, showing a better performance in female pediatrics compared to male pediatrics and an especially robust performance in female pediatrics ≤ 11 years.

Cross-sectional imaging of gastric cancer: pearls, pitfalls and lessons learned from multidisciplinary conference.

Gastric cancer is rising in prevalence associated with high mortality, primarily due to late-stage detection, underscoring the imperative for early and precise diagnosis. Etiology involves an interplay of genetic susceptibilities and environmental factors with a prominent role of Helicobacter pylori infection. Due to its often-delayed symptom presentation, prompt and accurate diagnosis is necessary. A multimodal imaging approach, including endoscopic ultrasound (EUS), multi-detector computed tomography (MDCT), and magnetic resonance imaging (MRI) is critical for accurate staging. Each modality contributes unique advantages and limitations, highlighting the importance of integrating diagnostic strategy. Moreover, multidisciplinary conferences offer a vital collaborative platform, bringing together specialists from diverse fields for treatment planning. This synergistic approach not only enhances diagnostic precision but also improves patient outcome. This review highlights the critical role of imaging in diagnosis, staging, and management and advocates for interdisciplinary collaboration in early detection and comprehensive management of gastric cancer, aiming to reduce mortality.

Molecular pathways and targeted therapies in head and neck cancers pathogenesis.

The substantial heterogeneity exhibited by head and neck cancer (HNC), encompassing diverse cellular origins, anatomical locations, and etiological contributors, combined with the prevalent late-stage diagnosis, poses significant challenges for clinical management. Genomic sequencing endeavors have revealed extensive alterations in key signaling pathways that regulate cellular proliferation and survival. Initiatives to engineer therapies targeting these dysregulated pathways are underway, with several candidate molecules progressing to clinical evaluation phases, including FDA approval for agents like the EGFR-targeting monoclonal antibody cetuximab for K-RAS wild-type, EGFR-mutant HNSCC treatment. Non-coding RNAs (ncRNAs), owing to their enhanced stability in biological fluids and their important roles in intracellular and intercellular signaling within HNC contexts, are now recognized as potent biomarkers for disease management, catalyzing further refined diagnostic and therapeutic strategies, edging closer to the personalized medicine desideratum. Enhanced comprehension of the genomic and immunological landscapes characteristic of HNC is anticipated to facilitate a more rigorous assessment of targeted therapies benefits and limitations, optimize their clinical deployment, and foster innovative advancements in treatment approaches. This review presents an update on the molecular mechanisms and mutational spectrum of HNC driving the oncogenesis of head and neck malignancies and explores their implications for advancing diagnostic methodologies and precision therapeutics.

Ultrasound imaging and shear wave elastography for the differential diagnosis of heel pain: a comparative cross-sectional study.

In correlation with magnetic resonance imaging (MRI), this study attempts to assess the effectiveness of the diagnostic of ultrasonography (US) features and shear wave elastography (SWE) in determining the different causes of heel pain. 55 heels with a mean age of 38.33 ± 10.8 were included in the study (10 control cases and 41 cases, 4 of which had bilateral heel pain). There were 23 female cases (56.1%) and 18 male cases (43.95%). Examinations using shear wave elastography (SWE) and ultrasound (US) were done in different positions. MRI and the obtained data were correlated. When used to diagnose different heel pain causes, ultrasound demonstrated great sensitivity and specificity. SWE demonstrated a good correlation with MRI findings and enhanced the ultrasound's diagnostic precision in identifying plantar fasciitis early on (increased accuracy from 88.9 to 93.33% with 100% sensitivity and 83.3% specificity) and Achilles tendinopathy (increased accuracy from 88.9 to 97.8 with 94.7% sensitivity and 100% specificity). In summary, we concluded that heel pain can be efficiently examined by both ultrasound (US) and shear wave elastography (SWE) with the former being used as the primary effective tool and the latter being done to increase diagnostic accuracy. We also concluded that SWE improved the ultrasound's diagnostic precision in identifying patients with early plantar fasciitis and Achilles tendinopathy and showed a robust relationship with clinical outcomes, enhancing patient evaluation and follow-up.

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making.

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BackgroundHead motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.MethodsFMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.ResultsIn total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).ConclusionsThe motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.

Comparison of Blood Gas Analysis and Auto-Analyzer Results for Sodium and Potassium Levels in Elderly and Non-elderly Adult Emergency Department Patients.

Objectives This study aims to evaluate the concordance between blood gas and biochemical measurement methods for sodium and potassium levels in elderly and non-elderly patients within an emergency department (ED) setting. Methods A retrospective method comparison study was conducted at an ED from February 1, 2023, to March 1, 2023. The study included 414 patients, categorized into "elderly" (aged 65 and above; n = 138, 33.3%) and "non-elderly" (aged 18 to 64; n = 276, 66.7%) groups. Concordance was assessed using Bland-Altman, Passing-Bablok, and Lin's concordance correlation methods. Results In sodium measurements, the elderly group exhibited an average bias of -1.52 mEq/L (95% confidence interval [CI] -2.12 to -0.92), with lower and upper limits of agreement (LoA) at -8.46 and 5.42 mEq/L, respectively, indicating a broader variance than non-elderly patients, who showed an average bias of -0.82 mEq/L with limits of -4.97 to 3.32 mEq/L. For potassium, the elderly group's average bias was -0.46 mEq/L (95% CI -0.36 to -0.57), with limits of agreement from -1.68 to 0.75 mEq/L, compared to non-elderly patients with a bias of -0.29 mEq/L and limits of -0.71 to 0.13 mEq/L. Furthermore, concordance correlation coefficients revealed a reduced agreement in the elderly for both sodium (r ccc = 0.799) and potassium (r ccc = 0.529) compared to the non-elderly cohort (sodium r ccc = 0.821, potassium r ccc = 0.715). Conclusion The study identifies significant discrepancies in sodium and potassium levels between elderly and non-elderly patients, suggesting a need for diagnostic precision. It emphasizes the importance of customizing diagnostic approaches to better serve the elderly population in EDs.

In vivo endoscopic optical coherence elastography based on a miniature probe.

Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). It offers high-resolution elasticity assessment with nanoscale tissue displacement sensitivity and high quantification accuracy, promising to enhance diagnostic precision. However, in vivo endoscopic OCE imaging has not been demonstrated yet, which needs to overcome key challenges related to probe miniaturization, high excitation efficiency and speed. This study presents a novel endoscopic OCE system, achieving the first endoscopic OCE imaging in vivo. The system features the smallest integrated OCE probe with an outer diameter of only 0.9 mm (with a 1.2-mm protective tube during imaging). Utilizing a single 38-MHz high-frequency ultrasound transducer, the system induced rapid deformation in tissues with enhanced excitation efficiency. In phantom studies, the OCE quantification results match well with compression testing results, showing the system's high accuracy. The in vivo imaging of the rat vagina demonstrated the system's capability to detect changes in tissue elasticity continually and distinguish between normal tissue, hematomas, and tissue with increased collagen fibers precisely. This research narrows the gap for the clinical implementation of the endoscopic OCE system, offering the potential for the early diagnosis of intraluminal diseases.

Role of Multidetector Computed Tomography with Contrast in Evaluation of Congenital Lung Malformations in Pediatric Age Group at a Tertiary Care Hospital: An Observational Study

Background: Computed tomography (CT) has a larger role in the noninvasive examination of congenital lung abnormalities, thanks to the introduction of multidetector CT (MDCT). If a surgical lesion is suspected, it is often necessary to use cross-sectional imaging, such as CT, for diagnosis confirmation, further lesion definition, and preoperative evaluation. Aims and Objectives: The aim of this study was to enhance comprehension of the occurrence and varieties of congenital lung abnormalities in children, as well as to assess and compare the diagnostic precision of MDCT with that of surgical intervention. Methodology: MDCT with contrast CT was performed in children with respiratory complaints and prenatal history of suspicion in targeted imaging for fetal anomalies (TIFFA) or congenital lung malformations (CLMs) using TOSHIBA 8 slice CT; the study population consisted of 40 pediatric patients with a history of chest anomalies during pregnancy in TIFFA or children with abnormal chest X-ray suspicion of lung anomalies for 18 months. Results: In a study of 40 pediatric patients who underwent MDCT with contrast to evaluate CLMs, 6 were excluded from further evaluation with contrast due to the absence of pulmonary anomalies on plain CT, leaving a possible diagnosis of s CLM in the remaining 34 cases. Of these, cystic pulmonary airway malformations (CPAM) (41.1%) were the most common, followed by congenital lobar emphysema (17.6%), bronchogenic cyst (14.7%), lung agenesis (11.6%), and phylogenetic lung. Conclusion: To notice the characteristics and related anomalies of CLMs and aid in the presurgical evaluation of CLMs, the study concludes that MDCT with contrast is the imaging of choice.

Accurate classification of glomerular diseases by hyperspectral imaging and transformer

Background and objectiveIn renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsistent results. There is a pressing need for innovative diagnostic tools that enhance traditional methods, streamline processes, and ensure accurate and consistent disease detection. MethodsIn this study, we present an innovative Convolutional Neural Networks-Vision Transformer (CVT) model leveraging Transformer technology to refine glomerular disease diagnosis by fusing spectral and spatial data, surpassing traditional diagnostic limitations. Using interval sampling, preprocessing, and wavelength optimization, we also introduced the Gramian Angular Field (GAF) method for a unified representation of spectral and spatial characteristics. ResultsWe captured hyperspectral images ranging from 385.18 nm to 1009.47 nm and employed various methods to extract sample features. Initial models based solely on spectral features achieved a accuracy of 85.24 %. However, the CVT model significantly outperformed these, achieving an average accuracy of 94 %. This demonstrates the model's superior capability in utilizing sample data and learning joint feature representations. ConclusionsThe CVT model not only breaks through the limitations of existing diagnostic techniques but also showcases the vast potential of non-invasive, high-precision diagnostic technology in supporting the classification and prognosis of complex glomerular diseases. This innovative approach could significantly impact future diagnostic strategies in renal disease research. Concise abstractThis study introduces a transformative hyperspectral image classification model leveraging a Transformer to significantly improve glomerular disease diagnosis accuracy by synergizing spectral and spatial data, surpassing conventional methods. Through a rigorous comparative analysis, it was determined that while spectral features alone reached a peak accuracy of 85.24 %, the novel Convolutional Neural Network-Transformer (CVT) model's integration of spatial-spectral features via the Gramian Angular Field (GAF) method markedly enhanced diagnostic precision, achieving an average accuracy of 94 %. This methodological innovation not only overcomes traditional diagnostic limitations but also underscores the potential of non-invasive, high-precision technologies in advancing the classification and prognosis of complex renal diseases, setting a new benchmark in the field.

The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools

The introduction of machine learning in digital pathology has deeply impacted the field, especially with the advent of whole slide image (WSI) analysis. In this review, we tried to elucidate the role of machine learning algorithms in diagnostic precision, efficiency, and the reproducibility of the results. First, we discuss some of the most used tools, including QuPath, HistoQC, and HistomicsTK, and provide an updated overview of machine learning approaches and their application in pathology. Later, we report how these tools may simplify the automation of WSI analyses, also reducing manual workload and inter-observer variability. A novel aspect of this review is its focus on open-source tools, presented in a way that may help the adoption process for pathologists. Furthermore, we highlight the major benefits of these technologies, with the aim of making this review a practical guide for clinicians seeking to implement machine learning-based solutions in their specific workflows. Moreover, this review also emphasizes some crucial limitations related to data quality and the interpretability of the models, giving insight into future directions for research. Overall, this work tries to bridge the gap between the more recent technological progress in computer science and traditional clinical practice, supporting a broader, yet smooth, adoption of machine learning approaches in digital pathology.

Detection of Liver Disease using ANN

Liver disease is an important worldwide health concern that requires early and correct diagnosis. This study studies the application of Artificial Neural Networks (ANN) for diagnosing liver disease based on clinical and biochemical data. An optimised ANN model was created and tested using measures like accuracy, sensitivity, specificity, and area under the ROC curve. The results reveal that the ANN outperforms standard diagnostic approaches and other machine learning algorithms, detecting liver illness with high accuracy even in complex scenarios. This research demonstrates the potential of ANN in boosting diagnostic precision and assisting clinical decision-making, paving the path for improved computational tools in medical diagnostics.

Point-of-care ultrasonography in cirrhosis-related acute kidney injury: How I do it

Discerning the etiology of acute kidney injury (AKI) in cirrhotic patients remains a formidable challenge due to diverse and overlapping causes. The conventional approach of empiric albumin administration for suspected volume depletion may inadvertently lead to fluid overload. In the recent past, point-of-care ultrasonography (POCUS) has emerged as a valuable adjunct to clinical assessment, offering advantages in terms of diagnostic accuracy, rapidity, cost-effectiveness, and patient satisfaction. This review provides insights into the strategic use of POCUS in evaluating cirrhotic patients with AKI. The review distinguishes basic and advanced POCUS, emphasizing a 5-point basic POCUS protocol for efficient assessment. This protocol includes evaluations of the kidneys and urinary bladder for obstructive nephropathy, lung ultrasound for detecting extravascular lung water, inferior vena cava (IVC) ultrasound for estimating right atrial pressure, internal jugular vein ultrasound as an alternative to IVC assessment, and focused cardiac ultrasound for assessing left ventricular (LV) systolic function and identifying potential causes of a plethoric IVC. Advanced POCUS delves into additional Doppler parameters, including stroke volume and cardiac output, LV filling pressures and venous congestion assessment to diagnose or prevent iatrogenic fluid overload. POCUS, when employed judiciously, enhances the diagnostic precision in evaluating AKI in cirrhotic patients, guiding appropriate therapeutic interventions, and minimizing the risk of fluid-related complications.

A Novel Approach for Tumor‐Released Methylated DNA Detection Using Molecularly Imprinted Polymers

AbstractThe convergence of tumor epigenetics and molecularly imprinted polymers (MIPs) holds promise in early cancer diagnostics. To the best of knowledge, none of the studies has achieved DNA methylation detection yet using MIPs and the proposed research will pioneer the application of MIPs for detecting DNA methylation, introducing a novel approach to enhance early diagnostic precision.