Search Results (1,131)

Purpose: Early detection of glottic cancer is essential for better outcomes, but current diagnostic methods remain largely invasive, highlighting the need for reliable non-invasive alternatives. Acoustic voice signals offer a non-invasive screening input, but their clinical value depends on whether signal-processing features and learned models can distinguish malignancy from acoustically similar benign lesions rather than merely separate pathological from healthy voices. Despite growing research on acoustic analysis combined with machine learning (ML) techniques, no systematic synthesis of this evidence currently exists. Therefore, the present review aimed to identify acoustic features that differentiate early glottic malignancies from both benign vocal fold lesions and normal vocal function using various ML techniques. Method: Following PRISMA 2020 guidelines, a comprehensive literature search was conducted across Scopus, EBSCO, Ovid (Embase), and PubMed databases through November 2025. Eighteen studies met inclusion criteria, and PROBAST was used for quality appraisal of included studies. Data extraction focused on acoustic feature categories, ML techniques, classification performance metrics, and validation strategies. Results: Nine acoustic feature categories emerged, with cepstral coefficients (particularly MFCCs) most prevalent across 55.6% of studies. Binary pathological-versus-normal classification achieved consistently high accuracy (96–98.3%) across both traditional machine learning and deep learning approaches. However, malignant-versus-benign discrimination demonstrated substantially lower performance (81–87.88% accuracy, AUC 0.631–0.91), with voice-only models proving insufficient. Multimodal models incorporating clinical variables or laryngoscopic images achieved higher performance, but their results cannot be attributed to voice features alone. All 17 prediction studies were judged at high overall risk of bias, predominantly because of small effective sample sizes, model-selection optimism, possible data leakage, and limited independent validation. Conclusions: ML can detect broad voice pathology, but current evidence is insufficient to support stand-alone acoustic screening for early glottic cancer. Future studies require clearly defined cancer-specific targets, patient-level analysis, standardized acquisition, nested model development, calibration, and independent multicentervalidation. Full article

Mycosis fungoides, the most common primary cutaneous T-cell lymphoma, presents notable diagnostic challenges. In its early stages, it may resemble inflammatory dermatoses both clinically and histologically, causing delays in diagnosis. These factors can lead to treatment delays, allowing the disease to progress and adversely affecting the patient’s quality of life. To address this issue, the use of skin imaging techniques like dermoscopy, reflectance confocal microscopy, optical coherence tomography, and high-frequency ultrasound has increased in recent years. These techniques assist in diagnosis and treatment monitoring, reducing unnecessary biopsies by guiding accurate sampling from the most relevant sites and improving staging accuracy. This review provides a thorough overview of non-invasive skin imaging methods employed in the diagnosis and management of mycosis fungoides. Full article

Accurate and efficient early pregnancy diagnosis (EPD) in livestock is crucial for optimizing breeding management and enhancing productivity in modern animal husbandry. Over the past century, EPD technology has evolved from empirical methods to sophisticated techniques, encompassing biochemical marker detection, ultrasonic imaging, and further extending to emerging non-invasive approaches such as infrared thermography (IRT) and spectroscopic analysis. These advancements have not only improved diagnostic accuracy but also broadened the research scope to include small livestock and multiple species. This review critically examines the historical evolution, current methodologies, and applications of EPD technology, with a focus on analyzing the advantages and limitations of both traditional and emerging techniques. Additionally, it explores the potential of multimodal fusion strategies and artificial intelligence (AI) in EPD. At present, machine vision, wearable monitoring, and several AI applications remain prospective approaches rather than validated tools for routine EPD. The conclusion highlights that, despite significant progress, current technologies still face limitations in achieving in situ, non-contact, and high-throughput detection. Looking ahead, the integration of cutting-edge technologies, such as AI, small wearable sensors, and physiological time-series data analysis, holds promise for overcoming these bottlenecks, enabling more intelligent and efficient pregnancy diagnosis, and providing scientific support for modern animal husbandry. Full article

Introduction: Voice-based digital biomarkers have emerged as a promising, non-invasive approach for the early detection and monitoring of neurodegenerative disorders, particularly Parkinson’s disease (PD). Although voice recordings can be acquired easily using mobile health technologies, their integration into routine clinical practice remains limited due to challenges related to model interpretability and clinical validation. This study aimed to investigate the diagnostic potential of voice recordings acquired through the TALIA smartphone and web-based platform and to improve model transparency using explainable artificial intelligence (XAI) techniques. Methods: Voice recordings from participants with PD and control group (CG) were acquired over a six-month period using the TALIA digital-health platform at Vito Fazzi Hospital in Lecce, Italy. The data were evaluated cross-sectionally at the recording level. Sustained vowel (/a/) phonations were preprocessed and transformed into spectrogram images. A transfer-learning framework based on the pre-trained VGGish convolutional neural network (VGGish-CNN) was developed to classify PD and CG voice recordings. To enhance interpretability, explainable artificial intelligence (XAI) methods, including Local Interpretable Model-Agnostic Explanations (LIME) and Occlusion Sensitivity, were integrated to identify the spectro-temporal regions contributing most strongly to model predictions. Results: The proposed VGGish-CNN framework demonstrated excellent classification performance in distinguishing PD from CG. The model achieved an accuracy of 0.93, precision of 0.91, recall of 0.95, F1-score of 0.93, loss of 0.16, and an area under the receiver operating characteristic curve (AUC) of 0.987. XAI analyses provided qualitative, sample-specific visualizations of the spectro-temporal regions contributing to individual model predictions, thereby improving the transparency and interpretability of the deep-learning model. Conclusions: The findings demonstrate that a transfer-learning approach based on VGGish-CNN could achieve promising recording-level classification performance in distinguishing voice recordings from participants with PD and CG within this pilot dataset. Furthermore, XAI techniques provide qualitative insight into the model’s decision-making process. These preliminary findings support further investigation of voice-based approaches for PD screening in larger cohorts using participant-level and external validation. Full article

Background: Invasive pneumococcal disease (IPD) is predominantly caused by Streptococcus pneumoniae and is associated with high mortality. This vaccine-preventable disease has been a significant public health concern, particularly among children < 5 years and adults > 50 years in India. Pneumococcal vaccines help prevent pneumococcal infections caused by certain strains of S. pneumoniae, but diagnostic challenges in India hinder disease management. Methods: The objective of this expert panel discussion is to deliberate upon the pathogenesis and epidemiology of S. pneumoniae in India, techniques for overcoming the diagnostic challenges in the isolation and detection of S. pneumoniae, and to explore opportunities for partnerships between clinicians and microbiologists to bridge gaps. A non-systematic, targeted literature search was conducted to identify studies supporting key themes of expert panel discussion. Eight microbiologists, one infectious disease, one pediatrics and neonatology, and one respiratory medicine specialist from different parts of India participated in the discussion. Results: The panelists reiterated that while culture is the gold standard, multiplex real-time polymerase chain reaction and BioFire^® FilmArray^® technology have been the most common diagnostic methods for the detection of S. pneumoniae in India, especially in tier 1 cities. Conclusions: This article elucidates the challenges faced by microbiologists and clinicians in isolating and detecting the pathogen, and proposes solutions to determine the true burden of pneumococcal disease in India. This information could further inform public health policy and vaccination strategies. Full article

Introduction: Cutaneous and subcutaneous tumors are frequently encountered in canine patients, posing diagnostic challenges in general veterinary practice. Heat-Diffusion Imaging (HDI) is an active dynamic thermography technique that evaluates tissue thermal changes following controlled stimulation. Integration of HDI with artificial intelligence (AI) provides a non-invasive approach for tumor classification. Objective: Our objective was to develop and internally validate HDI-based, tumor-specific AI classifiers for differentiating canine mast cell tumors (MCTs) and lipomas from other cutaneous and subcutaneous masses. Methods: This study included 669 dogs with 1010 cutaneous and subcutaneous masses. Dynamic thermal data were recorded from each mass using an HDI system. This was followed by cytological or histopathological diagnosis. Thermal features were extracted from the recordings and used to develop, train, and internally validate a primary AI-based classifier that distinguishes benign from malignant lesions. A diagnostically confirmed subgroup was used to train and internally validate tumor-specific classifiers for lipomas and mast cell tumors. The diagnostic performance of all classifiers was independently evaluated. Results: The primary classifier cohort comprised 952 masses. Of these, 760 masses were assigned to the training cohort and 192 masses to the test cohort. The primary classifier achieved a sensitivity of 92% (95% CI 83.6–96.3) in the test set of 192 masses, and a prevalence-adjusted negative predictive value (NPV) of 98.1% at an assumed malignancy prevalence of 15%. Among the 55 masses assessed by the lipoma classifier, sensitivity was 86.4% (95% CI 73.3–93.6) and specificity 100% (95% CI 74.1–100); all 38 masses flagged as lipoma were lipomas. Among the 81 masses assessed by the MCT classifier, sensitivity was 31.8% (95% CI 20.0–46.6) and specificity 94.6% (95% CI 82.3–98.5); all 16 masses flagged as MCT were malignant, and 14/16 were diagnosed as MCTs. Conclusions: These findings demonstrate that combining HDI-derived thermal features with AI enables promising, non-invasive differentiation of common canine cutaneous tumor types. The two-tiered diagnostic approach improves upon the primary classifier by providing tumor-specific classification. This strategy may be particularly valuable in general practice settings, where access to immediate cytological or histopathological evaluation can be limited. Full article

Gastric emptying is the process by which gastric contents are transferred from the stomach to the small intestine for further digestion and nutrient absorption. It is the result of multiple gastrointestinal mechanisms and is essential for understanding the kinetics of digestion in humans. Delayed gastric emptying is clinically relevant as it is commonly observed in conditions such as gastroparesis and functional dyspepsia. A wide range of diagnostic modalities are available to assess gastric emptying, including scintigraphy, ultrasonography, magnetic resonance imaging, gastric aspiration, and stable isotope breath tests. Scintigraphy is widely regarded as the ‘gold standard’ technique, as it enables a direct quantitative assessment of gastric emptying following ingestion of a radiolabeled meal(s). Scintigraphy is non-invasive, reproducible, widely available, and well tolerated; however, it lacks standardized protocols, is time-consuming, costly, and necessitates exposure to ionizing radiation. Although the radiation dose is low, cumulative exposure remains a concern, particularly in pediatric and pregnant populations. In contrast, ultrasonography is safe, non-invasive, portable, inexpensive, and free from radiation exposure. Nevertheless, the application of ultrasound is limited by operator dependency and the need for technical expertise, and image quality may be affected by factors such as obesity and intraluminal gas. Furthermore, there is substantial methodological variability across studies, and its use in assessing gastric emptying of semisolid and solid meals is less well established. 3D ultrasonography, in particular, shows promise as a safe, non-invasive method for assessing gastric emptying. Further studies are required to promote the consistent application of established scintigraphic gastric emptying protocols across centers and to validate 3D ultrasound, particularly with solid meals, to potentially enhance its role as an alternative to scintigraphy. This article represents a comprehensive review based on a structured literature search with the primary aim of comparing ultrasonographic and scintigraphic measurements of gastric emptying, including the advantages and disadvantages of both methods. Full article

Chronic kidney disease (CKD) represents a rising global health crisis traditionally monitored through routine biochemical markers that may not fully reflect underlying structural alterations. While renal biopsy remains the invasive reference standard for histopathological assessment, ultrasound elastography, particularly shear wave elastography (SWE), has emerged as a promising non-invasive imaging technique for assessing the mechanical properties of the renal parenchyma. This narrative review examines the role of renal elastography in adult nephrology, linking the pathophysiology of renal fibrosis with advances in clinical imaging. We discuss the biological mechanisms underlying changes in renal tissue biomechanics and review the available evidence regarding the association between elastography-derived stiffness measurements and histopathological fibrosis in native kidneys and renal allografts. Although several studies have demonstrated promising diagnostic potential, the clinical interpretation of renal stiffness remains challenging due to tissue anisotropy, renal perfusion and congestion, inflammation, obstruction, hydration status, body habitus, and acquisition-related factors. Current evidence supports renal elastography primarily as a potential adj method to conventional clinical, biochemical, and imaging assessment rather than as a replacement for kidney biopsy. Future integration of elastography with artificial intelligence, multiparametric ultrasound, and molecular biomarkers may further improve non-invasive renal phenotyping, although multicentre and longitudinal validation are required before widespread clinical use. Full article

Urinary incontinence, the involuntary leakage of urine, significantly impacts middle-aged women. Background/Objective: In response to the failure of traditional diagnostic methods, transperineal ultrasound (TPUS) imaging was developed. The aim of our study was to identify the utility of TPUS in combination with a quality-of-life questionnaire in stress urinary incontinence evaluation and its reflection in clinical outcomes. Methods: This retrospective study was conducted between 18 March 2022 and 31 December 2024 in the County Hospital Hunedoara, Romania, Department of Obstetrics and Gynecology. It included 58 patients who underwent surgery for stress urinary incontinence and a control group consisting of 50 patients without SUI. Quality of life scores were recorded preoperatively and 6 months postoperatively, and ultrasound examination was performed. Four variables (Q-type test, the expansion of the funnel shape of the proximal urethra, Doppler assessment, and bladder neck descent) were measured, and their preoperative and postoperative values were compared in the study and control groups. Results: Significant statistical differences were observed in the variables measured through TPUS before and after surgery in the study group (p = 0.0001). These characteristics were significantly different in the study group compared with control group (p = 0.0001). All women in the study group reported a severe impact on their quality of life compared with the control women (10% reported a moderate level) (p = 0.000). Additionally, quality of life was significantly improved in the study group after surgery (p = 0.0001). Conclusions: Transperineal ultrasound is a noninvasive, cost-effective, and highly reliable imaging technique used to assess SUI. Questionnaires are also useful in evaluating patients’ quality of life and satisfaction after surgery. Full article

Liver fibrosis staging plays a key role in the prognosis and management of chronic liver disease. Liver biopsy remains the reference standard for fibrosis assessment but is limited by its invasiveness and risk of complications, while magnetic resonance elastography, though also considered a gold standard, is constrained by cost and limited availability. Ultrasound-based elastography techniques—vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), and two-dimensional shear wave elastography (2D SWE)—have emerged as accessible non-invasive alternatives. This narrative review aimed to summarize the principles, diagnostic performance, advantages, and limitations of these three techniques across a range of etiologies. A comprehensive literature search was conducted across Scopus, PubMed, Embase, CINAHL, the Cochrane Library, Informit, and the JBI Database of Systematic Reviews and Implementation Reports, covering January 2015 to February 2026. Search terms included “vibration-controlled transient elastography”, “point shear wave elastography”, “two-dimensional shear wave elastography”, “liver stiffness measurement”, “non-invasive assessment” and “chronic liver disease”. International guidelines, systematic reviews and meta-analyses, large observational cohort studies, and narrative reviews were included in the synthesis. VCTE, pSWE, and 2D SWE all demonstrated good diagnostic performance for detecting advanced fibrosis and cirrhosis across multiple etiologies. VCTE was the most extensively validated technique, with standardized cut-offs endorsed by major international guidelines. pSWE and 2D SWE showed comparable diagnostic accuracy and can be integrated into conventional ultrasound systems, enabling simultaneous structural and stiffness assessment, although validated cut-off values for these two techniques are not yet established in current guidelines, and absolute stiffness values are not directly interchangeable across techniques, manufacturers, or software versions. Obesity, inflammation, steatosis and recent food intake were identified as factors affecting measurement accuracy across all three techniques. Ultrasound-based elastography techniques offer effective, non-invasive alternatives to liver biopsy for fibrosis assessment. VCTE remains the most validated option despite cost and availability constraints, while pSWE and 2D SWE offer comparable accuracy with practical integration advantages. Future research should focus on standardizing cut-off values, addressing confounding factors, and combining elastography with biomarkers and AI-driven models. Full article

Background/Objectives: Differentiating high-grade glioma recurrence from treatment-related changes remains challenging on conventional MRI. This study evaluated non-contrast arterial spin labeling (ASL) perfusion MRI for this distinction and compared its performance with dynamic susceptibility contrast (DSC) perfusion MRI. Methods: Postoperative follow-up MRI examinations obtained between November 2019 and May 2021 were retrospectively reviewed. The cohort included 63 MRI examinations from 36 adults treated for high-grade glioma. ASL, routine MRI, and DSC images were independently assessed by two neuroradiologists. Final diagnosis was based on histopathology or longitudinal clinical and imaging follow-up. Reader agreement, diagnostic performance, and an exploratory patient-level grouped machine learning analysis using ASL-only, DSC-only, and combined ASL–DSC features were evaluated. Results: ASL- and DSC-derived perfusion parameters were significantly higher in tumor recurrence than in treatment-related changes (all p < 0.001). Both techniques showed high diagnostic performance; DSC achieved the highest accuracy, whereas ASL provided high specificity across readers. Inter-reader agreement ranged from substantial to almost perfect. In grouped cross-validation, ASL-only, DSC-only, and combined models achieved mean AUCs of 0.946, 0.997, and 0.997, respectively. Permutation testing confirmed that combined-model performance exceeded chance expectations (empirical p = 0.002). Conclusions: Non-contrast ASL perfusion showed diagnostic performance comparable to DSC for differentiating high-grade glioma recurrence from treatment effects. ASL may provide a reliable non-invasive alternative for longitudinal surveillance, particularly when gadolinium administration is undesirable or contraindicated. Full article

Skin cancer represents a growing global health burden. Current diagnostic pathways combine clinical examination and dermoscopy with histopathological confirmation; however, overlap between benign and malignant lesions can create diagnostic uncertainty and lead to potentially avoidable biopsies. Electrical impedance spectroscopy (EIS) has emerged as a non-invasive technique with potential for portable and cost-efficient implementation that quantifies the dielectric contrast between malignant and healthy tissue, providing objective information that may support clinical decision-making. This review synthesizes the field across four levels. First, we describe the biophysical origins of the impedance contrast in skin cancer, spanning the cellular, tissue architecture, and molecular scales, together with the equivalent circuit and Cole–Cole frameworks used to interpret it. Second, we examine hardware advances, including electrode–skin interface strategies, flexible and wearable architectures, computational electrode design, and the translation from laboratory prototypes to commercial systems such as Nevisense. Third, we critically appraise clinical evidence from large multicenter trials, focusing on the sensitivity–specificity trade-off and the demonstrated reduction in the number needed to excise. Finally, we discuss emerging frontiers, including artificial intelligence-driven analysis and multimodal fusion with dermoscopy, reflectance confocal microscopy, optical coherence tomography, and near-infrared spectroscopy. We conclude that EIS is most valuable as a complementary component within an integrated, AI-supported multimodal diagnostic framework. Full article

Cardiac amyloidosis (CA) is an infiltrative cardiomyopathy caused by extracellular deposition of misfolded proteins, most commonly immunoglobulin light chains (AL) or transthyretin (ATTR). Once considered a rare disease, CA is increasingly recognised due to improved diagnostic strategies and the availability of disease-modifying therapies. Early diagnosis is crucial, as treatment efficacy and clinical outcomes are strongly influenced by the stage of cardiac involvement. Multimodality cardiac imaging plays a central role in the diagnostic pathway, risk stratification, and evaluation of therapeutic response in CA. Echocardiography represents the first-line imaging modality and is essential for raising clinical suspicion through the identification of characteristic structural and functional abnormalities, including ventricular wall thickening, diastolic dysfunction, and distinctive strain patterns. Bone scintigraphy has revolutionised the non-invasive diagnosis of ATTR-CA, allowing accurate identification of transthyretin-related disease in the absence of monoclonal gammopathy, which needs to be excluded via serum and urinary immunofixation. Cardiovascular magnetic resonance provides advanced tissue characterisation through late gadolinium enhancement and quantitative mapping techniques, enabling detection of early myocardial involvement and robust prognostic stratification. Emerging imaging modalities, including dual-energy (spectral) computed tomography and positron emission tomography tracers, show promise in myocardial amyloid quantification and subtype differentiation, although their role is still evolving. Integration of imaging findings with clinical and laboratory parameters allows comprehensive disease assessment, facilitating early diagnosis, guiding therapeutic decisions, and improving risk stratification. This review summarises the current role of multimodality imaging in CA, highlighting its contribution from early detection to prognostic evaluation and monitoring of treatment efficacy, with particular emphasis on the emerging role of quantitative imaging in monitoring treatment response. Full article

Background/Objectives: Fetal Alcohol Spectrum Disorder (FASD) is a preventable neurodevelopmental condition associated with dysfunction of fronto-subcortical networks affecting attention, executive function, and behavioral regulation. Central auditory processing difficulties frequently persist despite preserved peripheral hearing, complicating clinical evaluation when behavioral testing is limited or unreliable, and objective markers that do not depend on behavioral cooperation are therefore needed. This exploratory observational case study investigated whether Frequency-Following Responses (FFRs) can capture measurable within-subject change in speech-evoked neural encoding in a pediatric patient with FASD. Methods: FFRs were recorded at two time points bracketing an eight-session period of non-invasive superficial neuromodulation (NESA^®) in an 8-year-old girl with FASD, using speech syllables (/da/, /ba/, /ga/) presented monaurally at 80 dB SPL. Two-tailed block-level Mann–Whitney U tests with rank-biserial correlations compared peak latencies, sustained pitch-tracking metrics (pitch strength, pitch error), onset stimulus–response correlation, and signal-to-noise ratio. Results: Sustained pitch-tracking metrics differed between time points, with increased pitch strength and reduced pitch error, whereas onset peak latencies, onset cross-correlation, and signal-to-noise ratio remained stable. Peak C, marking the transition to the sustained portion of the response, occurred earlier at the post-assessment in all six stimulus–ear combinations. The differences were therefore selective to sustained encoding rather than to onset timing or recording quality. Conclusions: FFR-derived pitch-encoding metrics detected measurable within-subject change, providing proof of concept for the feasibility of FFRs as a candidate objective tool for longitudinal monitoring of speech-evoked neural encoding in pediatric FASD. This single-case observation does not evaluate diagnostic accuracy, sensitivity, specificity, or discrimination from typically developing children, and therefore does not establish FFR as a validated diagnostic technique. The findings are hypothesis-generating and motivate controlled longitudinal studies. Full article

Thalassemia represents the world’s most prevalent inherited hemoglobin disorder, affecting approximately 4.4 per 10,000 live births globally. Accurate genetic characterization is indispensable both for definitive diagnosis and for lifetime clinical monitoring. The past two decades have witnessed a paradigm shift from conventional protein-based assays toward comprehensive molecular techniques, including next-generation sequencing (NGS) and third-generation (long-read) sequencing, which in turn have enabled reproductive applications such as preimplantation genetic testing for monogenic disease (PGT-M) to identify unaffected embryos before implantation. (1) To systematically evaluate the molecular techniques available for confirming the diagnosis of alpha- and beta-thalassemia, including their diagnostic accuracy, indications, and limitations; (2) to examine how genotype–phenotype correlation and genetic modifier profiling inform clinical prognosis and therapeutic decision-making; and (3) to define evidence-based genetic monitoring parameters for longitudinal follow-up of patients receiving transfusions, iron chelation, and novel curative therapies including gene therapy. A comprehensive narrative review was conducted by systematically searching PubMed/MEDLINE for English-language peer-reviewed articles published between January 2000 and December 2024. Forty-three studies were ultimately included after applying predefined inclusion and exclusion criteria. Quality of included studies was assessed using SANRA (Scale for the Assessment of Narrative Review Articles). HPLC and capillary electrophoresis remain first-line phenotyping tools; DNA-based confirmation is mandatory for complete genotyping. Among known, previously characterized mutations, NGS-based targeted panels achieve > 95% detection sensitivity, but they require MLPA co-testing or long-read sequencing to detect structural variants such as large deletions. Genotype–phenotype prediction is substantially improved, though not rendered fully deterministic, by profiling three major modifier loci: XmnI (Gγ), BCL11A, and HBS1L-MYB. PGT-M using NGS achieves near-complete genotyping accuracy (>99%) with live birth rates of 40–60% per frozen embryo transfer cycle. For patients receiving curative gene therapy (exagamglogene autotemcel/Casgevy), molecular follow-up protocols spanning 15 years are now recommended. Cardiac T2* MRI remains the most reliable non-invasive tool for iron overload follow-up, superior to serum ferritin alone. A tiered, genotype-informed approach—combining HPLC/CE phenotyping, targeted molecular diagnostics, genetic modifier profiling, and periodic re-evaluation—optimizes diagnostic precision and guides individualized management across the thalassemia spectrum. Integration of PGT-M and long-read sequencing into standard care pathways, alongside robust gene therapy follow-up protocols, will define the next era of thalassemia genetics. Full article