Search Results (8,735)

Medical infrared thermography, which involves the use of infrared thermal cameras for the non-invasive assessment of skin surface temperature distribution, has gained increasing interest in recent years as a tool supporting diagnosis and treatment monitoring. The aim of this article is to present the historical background and critically reassess the current role of infrared thermography in medicine, with particular emphasis on standardization as a key determinant of its clinical utility. This Perspective highlights the fundamental impact of methodological variability on diagnostic performance and reproducibility. A structured framework for standardization is proposed, encompassing patient preparation, environmental conditions, device parameters and calibration, image acquisition protocols, region-of-interest definition and analysis, as well as reporting and clinical interpretation. The analysis demonstrates how inconsistencies at each of these levels reduce measurement reliability, limit inter-study comparability, and weaken clinical confidence in infrared thermography. The article also addresses the growing availability of mobile thermal imaging systems and their integration with artificial intelligence, while emphasizing the need for stronger evidence-based support across all methodological domains. The presented analysis suggests that, despite existing limitations, medical infrared thermography holds considerable potential as a supportive clinical tool. However, its broader clinical implementation remains limited by several factors, with the lack of standardized protocols constituting a major and practically addressable translational barrier. Wider adoption will require standardization efforts alongside rigorous validation studies and application-specific interpretative guidelines. Addressing these challenges through technological advances and coordinated international standardization may facilitate meaningful progress over the next decade. Full article

Background/Objectives: Periodontitis is a highly prevalent chronic disease requiring accurate diagnosis for effective treatment planning. Artificial intelligence (AI) has emerged as a potential tool to support clinical decision-making. This study aimed to compare the diagnostic performance and classification error patterns of a large language model (LLM) and a neural network (NN) in periodontitis classification according to the current staging and grading system. Methods: This retrospective study included 110 patients with periodontal disease. Clinical and demographic variables (age, sex, smoking status, number of teeth, API, BOP, PPD, and CAL) were analyzed. Reference diagnoses were established by two experts. Cases were evaluated using an LLM and a neural network. Model performance was assessed using accuracy, confusion matrices, and Cohen’s kappa coefficient, along with error analysis. Results: The LLM achieved 62% accuracy for stage and 63% for grade classification (κ = 0.48). The neural network showed higher performance, with 85% accuracy for stage and 79% for grade (κ = 0.79 and κ = 0.67, respectively). The LLM more often underestimated disease severity, whereas the neural network tended to overestimate progression. Differences between models were statistically significant (p < 0.0001). Conclusions: In this dataset and classification task, the task-specific neural network demonstrated higher diagnostic performance than the evaluated large language model. However, the findings should be interpreted in light of the fundamentally different training paradigms and intended applications of these AI systems. Further research is required to optimize and validate AI-based approaches for clinical use. Full article

Objectives: Multimodal large language models (MLLMs) have shown potential for medical image classification. We evaluated four optimization strategies in two MLLMs—GPT-4o (gpt-4o-2024-08-06) and Gemini 2.5 Flash-Lite—for ultrasound-based thyroid nodule malignancy classification using two public datasets and a clinical cohort of nodules with atypia of undetermined significance (AUS) cytology. Methods: Text prompting, few-shot learning, fine-tuning, and a hybrid strategy combining fine-tuning with few-shot learning were evaluated for each model. Performance was assessed using the Digital Database of Thyroid Images (DDTI; n = 80), a 1000-image test subset of TN5000, and an institutional AUS cohort with surgical pathology (n = 84). In the AUS cohort, the best-performing strategy was compared with the consensus classification of three endocrinologists and the American Thyroid Association (ATA) ultrasound risk stratification. Results: For GPT-4o, the hybrid strategy achieved the highest area under the receiver operating characteristic curve (AUC) in DDTI (0.866), TN5000 (0.689), and the AUS cohort (0.836). In the AUS cohort, its specificity was higher than that of endocrinologist consensus and ATA risk stratification when only high-suspicion nodules were classified as malignant (95.1% vs. 70.7% and 70.7%; p = 0.002 and p = 0.001, respectively), while sensitivity did not differ significantly (72.1% vs. 74.4% and 79.1%, respectively; both p > 0.05). However, the hybrid model misclassified 12 of 43 malignant nodules, corresponding to a false-negative rate of 27.9%. When high- and intermediate-suspicion ATA categories were classified as malignant, ATA sensitivity increased to 83.7% and specificity decreased to 56.1%; the hybrid model had a higher AUC than ATA risk stratification (0.836 vs. 0.749; p = 0.017). For Gemini 2.5 Flash-Lite, few-shot learning, fine-tuning, and the hybrid strategy did not improve AUC relative to text prompting in any dataset. Conclusions: The hybrid strategy produced the most consistent performance gains for GPT-4o across the three datasets but did not improve Gemini 2.5 Flash-Lite. The optimized GPT-4o model achieved high specificity in the diagnostically challenging AUS cohort, although its false-negative rate limits its use as a stand-alone diagnostic tool. Further validation in larger, prospective multicenter cohorts is required before clinical use. Full article

Background: Hemophilia care has undergone a major therapeutic transformation with the introduction of extended half-life products, non-replacement therapies, and gene therapy. However, the benefits of these advances are not equally distributed worldwide, and their impact on long-term musculoskeletal outcomes remains uncertain. Objective: To analyze global disparities in hemophilia care and research production in the context of recent therapeutic advances, with particular attention to musculoskeletal management, physiotherapy, and scalable strategies for resource-limited settings. Methods: A narrative review with a structured literature search was conducted. Two conceptual blocks were explored: global disparities and access to care in hemophilia, and recent therapeutic advances, including non-replacement therapies, extended half-life products, and gene therapy. Retrieved records were screened using Rayyan, and a structured workflow diagram was used to summarize the literature identification and selection process. A descriptive analysis was also performed to identify representative authors, institutions, and geographic patterns in hemophilia research. Results: The evidence shows substantial global disparities in diagnosis, access to treatment, healthcare infrastructure, and research production. Scientific output remains concentrated in high-income countries, while low- and middle-income regions are underrepresented. Advanced therapies consistently reduce bleeding rates and treatment burden, but concerns remain regarding access, affordability, durability, breakthrough bleeding, and long-term structural joint outcomes. Musculoskeletal complications, including subclinical bleeding and hemophilic arthropathy, remain clinically relevant despite improved hematologic control. Conclusions: The current paradigm shift in hemophilia care is not uniformly experienced worldwide. Addressing global disparities requires not only expanding access to advanced therapies, but also strengthening research capacity, implementing multidisciplinary care models, and integrating scalable interventions such as physiotherapy, patient education, and simplified diagnostic tools. Accessible musculoskeletal assessment strategies may help improve early detection, functional outcomes, and equity of care in resource-limited settings. Full article

Background: Mild Cognitive Impairment (MCI) is a clinical state between normal aging and dementia. It may involve impairment in one or several cognitive domains. MCI offers a key window for maintaining cognitive function and studying how deficits develop in the elderly, making it of great research value. Measurement tools for screening MCI are not yet standardized in China. The accuracy of diagnostic criteria and threshold values needs improvement. Previous studies on the neural mechanisms of MCI have examined various aspects, but the changes in the white matter microstructure in older adults with MCI remain unclear. Most past studies used Fractional Anisotropy (FA) analysis to examine changes in white matter fiber orientation, often ignoring fiber density. As a result, findings are often contradictory or difficult to interpret. Therefore, it is necessary to assess cognitive function in MCI populations using more comprehensive and standardized measurement tools. It is also important to explore the association between changes in white matter microstructure and cognitive function in MCI by analyzing FA and Mean Diffusivity (MD). Methods: First, we assessed cognitive function using the Cognitive Function Measurement Scale for the Elderly, developed by Beijing Normal University, with diagnoses based on the NIA-AA (National Institute on Aging—Alzheimer’s Association) criteria. Second, we employed Diffusion Tensor Imaging (DTI) combined with Tract-Based Spatial Statistics (TBSS) to investigate alterations in the white matter fiber tract integrity in individuals with MCI. Based on the metrics used, this study was divided into two analytical approaches: Analysis Mode 1 utilized FA to explore changes in white matter fiber orientation in the MCI group. Analysis Mode 2 utilized MD to examine changes in white matter fiber density in the MCI group. Third, we further explored the association between alterations in the white matter fiber tract integrity and cognitive function in individuals with MCI. Specifically, FA and MD values from brain regions showing significant differences between the MCI and normal control groups were extracted and correlated with cognitive test scores. Results: According to the results of the community measurement survey, the prevalence of MCI among the elderly in Shenzhen is approximately 21.54%. Individuals with MCI exhibited functional decline in memory, attention, language, executive function, and spatial processing. DTI results indicated that (1) FA values across the brain’s white matter fiber tracts showed a decreasing trend in the elderly with MCI, with no areas exhibiting significantly higher FA values. Specifically, FA values were significantly lower in the corpus callosum, internal capsule, corona radiata, thalamic radiation, external capsule, superior fronto-occipital fasciculus, and cingulum (cingulate gyrus). (2) White matter fiber tracts with significantly reduced FA values also demonstrated significantly increased MD values. Additionally, MD values in the cingulum (hippocampus), inferior cerebellar peduncle, and corticospinal tract were significantly reduced in the MCI group. (3) Correlation analysis revealed that the significant differences in FA and MD values within the white matter fiber tracts of older adults with MCI were correlated with scores on several cognitive tests. Conclusions: In the present study, older adults with MCI tended to exhibit functional decline across multiple cognitive domains and relatively extensive microstructural white matter damage. Observations suggested that white matter fiber density may be informative regarding these microstructural alterations, indicating that diffusion biomarkers in key regions such as the cingulum (hippocampus) warrant further investigation. Full article

Background/Objectives: Respiratory tract symptoms, urinary symptoms, and acute fevers frequently prompt emergency urgent care visits. Distinguishing bacterial from viral or non-bacterial etiology remains difficult because clinical features overlap and laboratory microbiological tests are often non-specific or delayed. The MeMed BV^® test is a rapid host-response assay that combines TRAIL, IP-10, and CRP into a composite score to differentiate between bacterial and viral/non-bacterial infections within 15 min. The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy and clinical utility of the MeMed BV test in adults and children with suspected respiratory tract infections, urinary tract infections, and undifferentiated fever. Methods: The review followed PRISMA-DTA guidelines. Medline, Embase, CINAHL, and the Cochrane Library databases were searched. The risk of bias was assessed using the QUADAS-2, Cochrane RoB 2.0, ROBINS-I, and JBI tools. Where appropriate, meta-analyses were performed using a bivariate random-effects or HSROC model. Results: Sixteen studies (12 diagnostic test accuracy (DTA) studies and four non-DTA studies) were included. The pooled sensitivity was 91% (95% CI: 86–94%), and specificity was 92% (95% CI: 91–93%), with consistent accuracy in adults (Sensitivity 93%/Specificity 91%) and children (Sensitivity 88%/Specificity 93%). The non-DTA studies demonstrated that MeMed BV-guided management improved antibiotic stewardship: antibiotics were prescribed in 20.6% of viral versus 73.2% of bacterial cases, and clinician adherence to MeMed BV results reached 75–80%. Conclusions: The MeMed BV test demonstrates consistently high diagnostic accuracy and is associated with improved antibiotic decision-making, supporting its integration into clinical workflows. Full article

The clinical relevance of food-specific IgG antibodies in pediatric gastrointestinal disorders remains controversial. Although current international guidelines discourage their use as standalone diagnostic tools, their significance within a broader immune–gut inflammatory framework has not been sufficiently explored. This study aimed to investigate associations between food-specific IgG reactivity, inflammatory and permeability biomarkers, microbiological findings, and abdominal ultrasound abnormalities in children with chronic gastrointestinal symptoms. Methods: (1) Children presenting chronic gastrointestinal symptoms associated with food-specific IgG polysensitization, elevated inflammatory and permeability biomarkers, and abdominal ultrasound abnormalities (number (n) = 196); (2) a symptomatic gastrointestinal group without the complete multimodal profile (n = 146); and (3) a control group with normal abdominal ultrasound findings and biomarkers within reference ranges (n = 210). All participants underwent food-specific IgG testing using a 216-antigen ELISA panel, abdominal ultrasound examination, and assessment of intestinal inflammatory and permeability biomarkers. Food-specific IgG antibodies were not interpreted as diagnostic markers of food allergy or food intolerance. Comparative analyses, correlation analyses, multivariable logistic regression, and receiver operating characteristic (ROC) analyses were performed. Results: Food-specific IgG polysensitization was significantly more frequent among children presenting the multimodal inflammatory profile compared with symptomatic and control groups (all p < 0.001). Reactivity predominantly involved gluten-containing cereals, dairy proteins, and mixed gluten–dairy patterns. Elevated fecal calprotectin, zonulin, and fecal histamine concentrations were more frequently observed in this subgroup, together with a higher prevalence of ultrasound abnormalities, including bowel wall thickening and mesenteric lymphadenopathy. Correlation analyses demonstrated significant associations between cumulative IgG burden and bowel wall thickness (r = 0.48, p < 0.001), while fecal calprotectin showed the strongest association with ultrasound abnormalities (r = 0.62, p < 0.0001). Multivariable logistic regression identified elevated calprotectin, increased zonulin, IgG polysensitization, and mixed gluten–dairy reactivity as independent predictors of pathological ultrasound findings. The integrated multimodal model demonstrated higher classification performance than isolated biomarkers. Conclusions: Children presenting chronic gastrointestinal symptoms, food-specific IgG polysensitization, inflammatory biomarker abnormalities, and ultrasound changes represented a multimodal clinical subgroup within the study population. These findings support evaluating food-specific IgG reactivity within a broader immune–gut assessment framework rather than as a standalone diagnostic biomarker. The observed associations should be considered exploratory and hypothesis-generating, requiring prospective validation and mechanistic investigation. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

Background/Objectives: Digital, remote, and ecological tools may complement clinic-based assessment in multiple sclerosis (MS), but the distribution of evidence across fatigue/fatigability, mobility, and real-world functional activity remains unclear. This scoping review mapped tools, metrics, constructs, contexts of use, and reported clinical utility in adults with MS, with attention given to whether the evidence was balanced across domains. Methods: Following Joanna Briggs Institute guidance and PRISMA-ScR/PRISMA-S reporting standards, five databases were searched on 14 March 2026. After deduplication, title/abstract screening, full-text assessment, and manual extraction and verification, the findings were synthesized descriptively without formal critical appraisal. Results: Of 3100 records identified, 1433 unique records were screened and 125 sources were included. Gait was the most frequently assessed domain (105/125), followed by fatigue/fatigability (33/125), physical activity (29/125), and sleep (2/125). The most frequent technologies were wearable devices (60/125), accelerometry (54/125), remote/home-based/telemonitoring modalities (52/125), and inertial measurement units (42/125). Conclusions: The evidence is predominantly gait- and mobility-focused, while fatigue/fatigability and broader real-world functional activity are less consistently represented. Reported clinical utility was usually framed around functional assessment, longitudinal/remote monitoring, rehabilitation planning, patient stratification, and decision support, but these characteristics were extracted as reported and were not independently appraised. Full article

Major depressive disorder (MDD) is clinically heterogeneous, and peripheral inflammatory biomarkers may help clarify early biological mechanisms before illness chronicity or pharmacologic treatment confound interpretation. This systematic review synthesized evidence on peripheral inflammatory biomarkers in first-episode, drug-naïve major depressive disorder (FEDN-MDD) compared with healthy controls and examined associations with clinical severity. Following PRISMA 2020, searches of PubMed/MEDLINE, Embase, PsycINFO, and Scopus from inception to 19 March 2026 identified 313 records; after screening, 16 publications were included in qualitative synthesis. Studies varied in age group, biological matrix, assay platform, and statistical reporting, precluding meta-analysis. The most frequently assessed biomarkers were IL-1β, TNF-α, IL-6, and CRP/hs-CRP. IL-6 showed the clearest recurrent tendency toward elevation in FEDN-MDD, whereas CRP/hs-CRP findings were partially positive but methodologically limited. TNF-α and IL-1β findings were mixed, and clinical correlations with depressive severity were sparse and inconsistent. Overall, the evidence supports heterogeneous early immune dysregulation in a subset of patients with FEDN-MDD rather than a single reproducible inflammatory signature. Peripheral inflammatory biomarkers should currently be considered research tools for biological stratification and mechanistic hypothesis generation, pending larger standardized longitudinal studies. Full article

Background/Objectives: White–Sutton syndrome (WHSUS; OMIM 616364) is a rare neurodevelopmental disorder caused by pathogenic variants in the POGZ gene and characterized by developmental delay, intellectual disability, speech impairment, autism spectrum features, and dysmorphic traits. Although most reported cases are sporadic, inherited forms are exceptionally rare. We describe a familial case of WHSUS involving an affected mother and two children carrying a heterozygous POGZ nonsense variant, highlighting marked intra-familial phenotypic variability and expanding the clinical spectrum of the disorder. Methods: Clinical evaluation included multidisciplinary assessments. Genetic testing was performed using clinical exome sequencing (CES) with a virtual neurodevelopmental disorder (NDD) gene panel, followed by Sanger confirmation and segregation analysis in family members. The POGZ transcript reference NM_015100.3 was used for variant nomenclature and verified with the Mutalyzer tool. CNV detection from NGS data was performed using the Alissa CNV caller (Agilent) and visualized via IGV; the Xp11.22 microduplication was confirmed by chromosomal microarray (aCGH) and parental segregation analyses. Results: CES identified the heterozygous pathogenic POGZ variant c.1522C>T (p.Arg508*) in the female proband (III₆), an infant presenting with global developmental delay, hypotonia, speech impairment, gait abnormalities, and characteristic dysmorphic features. Segregation analysis demonstrated maternal inheritance and confirmed the presence of the variant in her affected brother (III₄), who also carries a de novo 1.79 kb microduplication at Xp11.22, while the maternal grandparents tested negative, indicating a de novo origin in the mother. The mother exhibited an attenuated phenotype, including mild neuropsychiatric and gastrointestinal manifestations. The variant is predicted to undergo nonsense-mediated decay (NMD), consistent with a moderate clinical presentation; however, experimental validation was not performed. Conclusions: This report documents a rare familial occurrence of WHSUS with highly variable expressivity. Our findings broaden the phenotypic and molecular characterization of POGZ-related disorders and emphasize the importance of comprehensive segregation studies and early genomic diagnosis. While experimental data link POGZ deficiency to DNA repair defects, no longitudinal clinical studies have demonstrated increased cancer risk in WHSUS; therefore, formal malignancy screening guidelines cannot be established at present, and this issue deserves future study in larger cohorts or registries. Full article

Background/Objectives: Enzalutamide and abiraterone acetate are commonly used androgen receptor pathway inhibitors in metastatic castration-resistant prostate cancer (mCRPC), including after docetaxel. However, real-world outcomes remain heterogeneous, and simple prognostic markers may help describe this variability. This study aimed to describe survival outcomes with enzalutamide and abiraterone acetate after docetaxel and to explore the prognostic value of a routine clinical-inflammatory risk classification. Methods: This retrospective single-center study included 136 patients with mCRPC treated with enzalutamide or abiraterone acetate after docetaxel. A composite risk classification was defined using four routinely available variables: pan-immune-inflammation value (PIV) > 457.99, time to castration resistance < 12 months, baseline hemoglobin ≤ 12 g/dL, and Gleason score ≥ 8. One point was assigned for each adverse factor, and patients were classified as low, moderate, or high risk. Overall survival (OS) was assessed using Kaplan–Meier estimates and Cox regression. The prognostic score and Cox regression-based nomogram were evaluated as exploratory tools. Results: Of the 136 patients, 8 (5.9%) were classified as low risk, 67 (49.3%) as moderate risk, and 61 (44.9%) as high risk. Median OS was not reached in the low-risk group, compared with 33.84 months in the moderate-risk group and 9.66 months in the high-risk group. In multivariable analysis, high-risk status was independently associated with worse OS (HR = 9.87; 95% CI: 2.38–40.92; p = 0.002). No statistically significant OS difference was observed between enzalutamide and abiraterone acetate in this non-randomized cohort (HR = 1.36; 95% CI: 0.90–2.06; p = 0.142). Conclusions: In this real-world post-docetaxel mCRPC cohort, no statistically significant OS difference was observed between enzalutamide and abiraterone acetate; however, the study was not designed to establish comparative effectiveness or therapeutic equivalence. The exploratory risk classification based on routine clinical and inflammatory variables was associated with distinct survival outcomes. External validation is required before clinical application. Full article

Radiation-induced oral mucositis (RIOM) frequently causes severe pain and treatment interruptions in patients with head and neck cancer. While earlier guidelines suggested zinc supplementation, updated MASCC/ISOO guidelines downgraded it to ‘No Guideline Possible’ due to highly conflicting evidence. This study aims to resolve these inconsistencies by evaluating zinc’s prophylactic efficacy and investigating whether the route of administration determines its clinical benefit. Following PRISMA guidelines and INPLASY registration (INPLASY202620063), we searched PubMed, Embase, and the Cochrane Library through February 2026. We included randomized controlled trials (RCTs) comparing prophylactic zinc versus placebo or standard care in head and neck cancer patients receiving radiotherapy. Risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. The primary outcome was severe (Grade 3–4) RIOM incidence. Data from five RCTs (332 patients) were pooled using a random-effects model. Overall, zinc significantly reduced severe mucositis risk (RR = 0.35, 95% CI: 0.17–0.73, p = 0.005). Crucially, an exploratory subgroup analysis revealed a striking divergence based on delivery route. Topical zinc mouthwash demonstrated encouraging protection (RR = 0.16, 95% CI: 0.05–0.49, p = 0.001) with zero heterogeneity (I² = 0%). In contrast, systemic zinc yielded borderline, inconsistent benefits (RR = 0.52, 95% CI: 0.27–1.01, p = 0.055, I² = 37%). In conclusion, the localized pool of contemporary evidence clearly demonstrates that the systemic oral ingestion of zinc supplements does not provide a reliable prophylactic benefit against severe radiation-induced oral mucositis in head and neck cancer care. Conversely, topical zinc mouthwashes exhibit an encouraging protective trend; however, the severe paucity of available randomized trials and low cumulative patient volume preclude definitive clinical verification. While these exploratory findings suggest that topical administration may provide a more consistent protective trend compared to systemic routes, they should be interpreted as hypothesis-generating rather than definitive. Future large-scale, multi-center RCTs are strictly warranted to validate these promising route-specific benefits before formal guideline integration. Full article

Background: Cardiac point-of-care ultrasound (POCUS) is widely used in emergency and acute care settings. Still, broader use remains limited by operator dependence and variability in image acquisition and interpretation. Artificial intelligence (AI), including machine learning and deep learning methods, has been applied to cardiac POCUS to support image acquisition, automate quantitative measurements, and assist interpretation. Methods: We performed a narrative review of current applications of AI-assisted cardiac POCUS. A targeted literature search of PubMed and Google Scholar from 2018 to 2026 was conducted using terms related to AI, machine learning, deep learning, and cardiac ultrasound. Studies evaluating AI-assisted cardiac ultrasound in clinical, educational, or image-acquisition settings were included, with emphasis on recent, clinically relevant applications. Results: The most developed application of AI-assisted cardiac POCUS is an automated assessment of left ventricular systolic function, particularly the left ventricular ejection fraction (LVEF), where multiple studies report agreement with expert interpretation or formal echocardiography and improved performance among novice users. AI-assisted tools have also been evaluated for pericardial effusion detection, guidance for image acquisition, and education. More complex applications, including diastolic function assessment and hemodynamic measurements such as LVOT-VTI, remain less well validated and more dependent on image quality. Across studies, performance is closely linked to image acquisition quality and has often been evaluated under controlled rather than real-world conditions. Conclusions: Current evidence supports AI-assisted cardiac POCUS primarily as a decision-support tool, with the strongest data for automated assessment of LVEF. Other applications remain investigational. Full article

Background/Objectives: Foot and ankle presentations during Hajj occur in a dense mass-gathering environment where prolonged walking, heat exposure, crowding, variable footwear, and limited self-care can interact with chronic disease and wound vulnerability. Previous Hajj studies have described foot injuries and diabetes-related complications, but less is known about whether simple high-risk foot documentation flags identify presentation records with higher care-pathway intensity. The primary objective was to estimate the presentation-level burden of core high-risk foot profiles among pilgrims presenting with foot and ankle conditions during Hajj 2025. Secondary objectives were to evaluate associations with a surgery-coded care-intensity indicator, hospital referral, and component heterogeneity. Methods: This observational presentation-level analysis included 3957 foot and ankle presentation records. The unit of analysis was the presentation/case record, not a unique individual pilgrim. A core high-risk foot profile was defined as diabetes, neuropathy, diabetic foot ulcer, foot ulcer, complications of open wound, or osteomyelitis. The primary outcome was a surgery-coded care-intensity indicator, defined solely from treatment documentation containing “Surgery” and interpreted as a care-pathway proxy rather than confirmed operating-room surgery. Logistic regression estimated crude and adjusted odds ratios (ORs); exploratory risk-category analyses assessed heterogeneity within the composite profile. Results: Core high-risk foot profiles were identified in 1793/3957 presentations (45.3%). The primary outcome occurred in 239/1793 high-risk presentations (13.3%) and 201/2164 non-high-risk presentations (9.3%), an absolute difference of 4.0 percentage points. The crude OR was 1.50 (95% CI 1.23–1.83; p < 0.001). The association persisted in the primary adjusted model (adjusted OR 1.47; 95% CI 1.20–1.79; p < 0.001) and in the extended clinical sensitivity model (adjusted OR 1.47; 95% CI 1.20–1.80; p < 0.001). Care pathways and secondary outcomes are summarized was also more frequent in high-risk presentations (12.2% vs. 9.8%; crude OR 1.28; 95% CI 1.05–1.57; p = 0.017). Exploratory category analysis showed that chronic-risk-only presentations had a primary outcome rate similar to non-high-risk presentations (9.0% vs. 9.3%), whereas ulcer/wound/deep-infection presentations had a higher rate (17.3%; crude OR 2.04; 95% CI 1.63–2.55; p < 0.001). Model discrimination was modest (C-statistics 0.55–0.64). Conclusions: Core high-risk foot flags were common among Hajj foot and ankle presentation records and were associated with surgery-coded care-intensity and referral documentation. However, the composite was clinically heterogeneous, the outcome was not a validated surgery endpoint, and the models were not prediction tools. These findings support cautious use of high-risk foot flags as operational prompts for assessment and pathway planning rather than as standalone clinical risk estimates. Full article