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PubMed2026

Assessment of atypical glandular cell interpretation in Pap tests using the Hologic Genius Digital Diagnostics System.

Atypical glandular cells (AGC) are a diagnostic challenge. The aim of this study was to evaluate the efficacy and diagnostic performance of AGC detection on the Hologic Genius Digital Diagnostics System (HGDDS). A retrospective analysis of 451 ThinPrep Pap cases was conducted, including 207 cases of AGC, 27 cases of high-grade squamous intraepithelial lesion (HSIL), 25 cases of low-grade squamous intraepithelial lesion (LSIL), and 192 benign cases. All AGC cases had follow-up histologic diagnoses, with 66 cases subsequently diagnosed as adenocarcinoma. The slides were randomized, scanned, and analyzed by the HGDDS. Patient age and HPV test results were provided to reviewers, an experienced cytologist, who screened the cases, followed by two cytopathologists who independently examined the cases on the HGDDS. Diagnostic concordance between the two cytopathologists indicated strong agreement (κ = 0.829). Sensitivity of AGC on Papanicolaou (Pap) tests for adenocarcinoma detection on HGDDS was 98.5% and 95.5%, respectively, comparable to the original ThinPrep interpretation (OTPI). Specificity for adenocarcinoma detection was significantly higher (84.6% and 85.6%) with the HGDDS than 27.7% with OTPI. Overall, the diagnostic performance for AGC/HSIL interpretation to detect CIN2/3/adenocarcinoma appeared to have improved with HGDDS compared with OTPI, particularly for specificity and positive predictive value (PPV). This is the first study evaluating AGC diagnosis using the HGDDS. The findings demonstrate that the sensitivity of adenocarcinoma detection as AGC on HGDDS is comparable to the ThinPrep Imaging System, but the specificity and PPV are improved. This suggests the potential of artificial intelligence to augment the performance of cervical cancer screening.

Humans↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. OBJECTIVE: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG-derived inputs. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. RESULTS: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92-0.96; 95% PI 0.71-0.99), 0.87 (95% CI 0.84-0.89; 95% PI 0.66-0.96), and 0.83 (95% CI 0.79-0.87; 95% PI 0.61-0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69-0.84; 95% PI 0.30-0.96), 0.81 (95% CI 0.75-0.85; 95% PI 0.39-0.96), and 0.91 (95% CI 0.87-0.94; 95% PI 0.55-0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG-derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. CONCLUSIONS: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG-derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG-derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.

Humans↗

PubMed2026

Socket motility assessment of anophthalmic sockets: a systematic review.

PURPOSE: Systematically review and categorize the methods used to assess socket and prosthetic motility in anophthalmic patients following enucleation or evisceration. METHODS: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Embase, Web of Science, and Scopus were searched from inception through September 2024. Studies reporting qualitative or quantitative assessments of motility in anophthalmic sockets or ocular prostheses were included. Motility assessment methods were categorized as qualitative (descriptive or graded clinical evaluation) or quantitative (numerical measurements in millimeters, degrees, or objective tracking systems). RESULTS: Thirty-five studies encompassing 1,819 patients met inclusion criteria. Nineteen studies used qualitative assessment methods, including subjective observation, graded scales based on cardinal gaze positions, or comparison with the contralateral eye. Sixteen studies employed quantitative techniques, such as the Kestenbaum limbus test, Lister perimeter measurements, conjunctival or limbal markings, photographic image analysis, infrared eye-tracking systems, and magnetic search-coil technology. Considerable heterogeneity was observed in measurement techniques, reporting standards, timing of assessment, and distinction between socket and prosthetic motility. CONCLUSIONS: Substantial variability exists in the methods used to assess motility in anophthalmic sockets, limiting comparability across studies. Establishing standardized, feasible, and reproducible assessment approaches may improve outcome reporting and facilitate meaningful comparisons in future oculoplastic research.

Humans↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2026

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability of machine learning performance and the recurrence of biological signals across datasets require cautious interpretation. We developed an explainable artificial intelligence framework spanning two fluids and four ADNI proteomic datasets, covering 2082 modality specific samples, all analysed internally within ADNI. Phase 1 analysed plasma using a 119 analyte NULISA and targeted UPENN panel (n = 727; 216 CE, 511 controls). Phase 2 extended the analysis to CSF using SOMAscan7k, TMT-MS and targeted SET2, with Elecsys Aβ42, Aβ40, total tau and p-tau181 as anchor biomarkers. Only SOMAscan was subject-independent relative to Phase 1 plasma; TMT-MS and SET2 overlapped with Phase 1 for 96.0% and 97.7% of subjects and therefore are not independent replication cohorts. Under subject-level splits with fold internal preprocessing, we compared Elastic Net, Explainable Boosting Machines and gradient boosted trees with SHAP-based explanations. Among the candidate pipelines, we selected the pipeline with the highest held-out test ROC AUC for each platform; the selected values were 0.927 in plasma and 0.954-0.973 across the three CSF datasets. Because the same held out test performance was used for pipeline selection and headline reporting, these are optimistically selected single-holdout estimates, not unbiased estimates of generalizable or clinical performance. Explanations identified five recurring biological axes within ADNI: cholinergic (ACHE), tau/14-3-3 (YWHAG, YWHAZ, YWHAB, YWHAE), neuro-axonal (NEFL, NEFH), microglial/complement (CHIT1, SMOC1, CHI3L1, C7, CFH) and synaptic (NPTXR, NPTX2, DLG4, SYT5, VSNL1, ELAVL2). CSF analyses showed synaptic vesicle-cycle enrichment (q = 2 × 10-6), and CSF YWHAG correlated strongly with total tau (ρ = 0.87). Cross-fluid directional concordance was modest overall (54-57%) but increased to 73-80% among mapped analyte/protein rows reaching q < 0.05 in CSF. These findings provide hypothesis-generating, internally supported evidence within ADNI. Independent external cohorts with locked pipelines are required to evaluate generalizable performance and biological reproducibility; the overlapping TMT-MS and SET2 analyses should not be interpreted as independent replication.

Alzheimer Disease↗

PubMed2026

Applications of metal-organic frameworks in smart packaging for food freshness indication: a comprehensive review.

Smart packaging is extensively studied for its multifunctional capabilities in antimicrobial activity, preservation, and atmosphere modification. Recently emerged metal-organic frameworks (MOFs) freshness-indicating packaging becomes a key research direction in smart packaging owing to its distinctive functions and physicochemical properties. As multifunctional materials, the unique porous structure and tunable properties of MOFs provide a distinctive approach for developing food packaging applications dedicated to food freshness indication. Existing MOFs-based smart packaging still faces potential safety risks and technical challenges in practical applications, and there remains a lack of integrated discussion that combines synthesis strategies, packaging design, optimization, and safety assessment. This review elaborates on the application of MOFs in freshness-indicating smart packaging, focusing on diverse MOFs synthesis strategies, the formats of smart packaging, types of indicator signals, and qualitative/quantitative analytical methods. It also delves into the methodology concepts of MOFs-based smart packaging and evaluates MOFs safety in food packaging by addressing potential risks. Studies show that MOFs-based smart packaging achieves qualitative and semi-quantitative analysis of food freshness through multiple signal modalities such as visible color change, fluorescence, and photothermal effects. This review emphasizes that safe MOFs design is critically important and should comply with the overall migration limit of <10 mg/dm2 specified in Regulation (EC) No 1935/2004, lanthanide element limit of <0.05 mg/kg, and FDA threshold of 1.5 μg/person/day. Comprehensive safety assessment and intelligent sensing platforms will constitute pivotal directions for advancing MOFs-based smart packaging toward practical application.

Food Packaging↗

PubMed2026

AI Health message intervention: The role of message customization and message source in breast cancer screening among women of color.

OBJECTIVES: To examine the effectiveness of breast cancer screening messages with varying levels of customization (generic, targeted, and tailored) and to compare AI-generated versus human-generated messages. METHODS: A between-subjects experimental design with a control condition was employed. Message content followed a standardized structure and varied by level of customization: generic, targeted (demographic-based), and tailored (perceived susceptibility- and barrier-based). Messages were developed by either the authors or GenAI (ChatGPT-4o). A total of 391 participants recruited via Prolific were randomly assigned to five groups (generic, targeted-human, targeted-AI, tailored-human, and tailored-AI). Self-efficacy, behavioral intentions, attitudes, and message believability were measured using different scales. RESULTS: Customized (tailoring and targeting) health messages performed comparably to generic messages in shaping positive health outcomes. GenAI-generated messages also produced outcomes comparable to those of human-generated messages under standardized conditions. Significant negative indirect effects through message believability for the human-tailored condition was found relative to the generic condition. CONCLUSIONS: GenAI may be a useful tool for developing and customizing scalable health messages. Its effectiveness depends not only on customization but also on maintaining message quality, including readability, clarity, coherence, naturalness, and credibility. PRACTICAL IMPLICATIONS: GenAI may support health practitioners in developing customized and scalable breast cancer messages. However, professional review remains necessary to ensure that the message is culturally appropriate, responsive to patient concerns, and suitable for use alongside patient-provider communication.

Humans↗

PubMed2026

Fundamentals of pacemakers ECG interpretation - part 2.

BACKGROUND: Modern pacemakers incorporate arrhythmia-response algorithms, ventricular pacing minimization protocols, and safety mechanisms that generate ECG patterns indistinguishable from pathological AV block, sensing malfunction, or device-mediated tachycardia. Failure to recognize these algorithm-driven signatures leads to unnecessary interventions, misdiagnosis, and inappropriate device reprogramming. This manuscript is the second in a two-part series on pacemaker ECG interpretation. METHODS: We conducted a narrative review of peer-reviewed literature and device-specific documentation on algorithm-driven ECG behavior, synthesizing evidence across arrhythmia recognition, upper rate physiology, ventricular pacing minimization, mode switching, safety mechanisms, and hysteresis algorithms. RESULTS: Pacemaker-mediated tachycardia produces regular paced wide-complex tachycardia locked at the upper tracking rate, initiated by any event with retrograde VA conduction. Ventricular tachycardia is identified by QRS morphology diverging from the known paced pattern, absent pacing spikes, and AV dissociation. Upper rate Wenckebach behavior mimics Mobitz type I AV block; 2:1 upper rate response mimics second-degree AV block. Ventricular pacing minimization algorithms produce isolated nonconducted P waves and prolonged AV intervals that simulate pathological conduction disease. Mode switching causes abrupt rate drops misidentified as output failure. Ventricular safety pacing generates a conspicuously short, fixed AV interval. Three discrete pacing artifacts reflect AV-sequential cardiac resynchronization therapy (CRT), ventricular safety pacing in CRT, or His-bundle pacing with backup RV output. Rate and AV hysteresis produce pauses and wandering AV intervals mimicking oversensing or Wenckebach periodicity. CONCLUSIONS: Recognizing algorithm-driven ECG patterns requires knowledge of device timing intervals and refractory periods, which lets clinicians distinguish programmed behavior from true malfunction or cardiac arrhythmia.

Humans↗

PubMed2026

Interventions with a significant mortality difference in acute respiratory distress syndrome: A systematic review and comparison with Guidelines.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) has a high mortality rate. European Society of Intensive Care Medicine (ESICM) and American Thoracic Society (ATS) Guidelines are the worldwide reference for clinicians in management of ARDS. Mortality represents one of the most important outcomes in intensive care practice and randomized controlled trials (RCTs) the highest level of evidence. We compared Guidelines recommendations with RCT results to highlight differences and find potential new therapeutic opportunities. METHODS: We performed a systematic review of all RCTs reporting a statistically significant mortality difference in ARDS and a subsequent comparison with ESICM and ATS Guidelines recommendations. RESULTS: We identified 33 RCTs and 23 interventions with mortality difference in ARDS patients. Seven interventions relate to invasive ventilation strategies, two to noninvasive ventilation strategies, one to extracorporeal membrane oxygenation (ECMO), 12 to drugs and one to nutritional support. In 25/33 (76%) RCTs the intervention was associated with mortality reduction and in 8/33 with mortality increase (24%). Multicenter studies were 24/33 (73%) while blinding was adopted in 19/33 (58%) studies. Guidelines recommendations supported by RCTs with mortality impact include: the use of low tidal volume ventilation, prone positioning, venovenous ECMO, steroids and the avoidance of high frequency oscillatory ventilation. Eight of the interventions identified were not mentioned by Guidelines but demonstrated reduced mortality, and five further interventions demonstrated increased mortality. CONCLUSIONS: This systematic review highlights potential gaps between RCTs results and Guidelines that could be used to plan future research or highlight topics to be discussed in future Guidelines.

Humans↗

PubMed2026

Improving survival in Duchenne muscular dystrophy across eras: a systematic review and cumulative meta-analysis.

BACKGROUND: Duchenne muscular dystrophy (DMD) was historically associated with death in the late teens or early twenties, mainly from respiratory failure. Survival has improved substantially with home mechanical ventilation (HMV) and multidisciplinary care, although variability remains. This study evaluated temporal trends in survival in DMD and the impact of HMV. METHODS: A study-level cumulative meta-analysis (PROSPERO CRD420251163011) of studies reporting survival outcomes in patients with DMD was conducted (PubMed 1977 to 13 October 2025). Pooled estimates of median survival were calculated, and random-effects meta-analyses with predefined subgroups (HMV and study period) were performed, alongside meta-regressions. Risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: 53 studies (median follow-up 8 years), comprising more than 13,000 patients, of whom 60% received HMV, were included. Median survival differed substantially between ventilated (29 years, 95%CI 27 to 31) and non-ventilated (19 years, 95%CI 18 to 20) patients. Survival improved progressively over time in both groups. Glucocorticoid therapy was not associated with improved survival (p=0.45), whereas treatment with heart failure medications, including renin-angiotensin system inhibitors (p=0.002) and β-blockers (p=0.02), was associated with longer survival. The predominance of mortality shifted from respiratory to cardiac causes, while enhanced cardiac management was associated with a growing contribution of other causes of death. CONCLUSION: Survival in DMD has increased substantially over time, with median survival now approaching the third decade of life among ventilated patients. The growing contribution of cardiac and other non-respiratory causes of death highlights the importance of long-term multidisciplinary and early cardioprotective intervention. STUDY REGISTRATION: The meta-analysis and systematic review have been registered on PROSPERO (CRD420251163011).

Humans↗

PubMed2026

Anabolic androgen therapy in critically ill adults: A systematic review and meta-analysis.

Critical illness is characterized by a catabolic, proinflammatory state. Anabolic agents, such as testosterone, have therefore been proposed as therapeutic targets. Our objectives were to assess the effects of testosterone in critically ill populations on patient-important outcomes and identify design limitations to inform future studies. We searched for randomized control trials (RCTs) through Medline, Embase, and EBM Reviews databases from inception through February 24, 2026, including English language articles enrolling adults (≥18 years) admitted to ICU where anabolic androgen therapies (AAT) were compared with placebo or standard of care. Studies had to report at least one of: mortality, ICU and hospital lengths of stay, or duration of mechanical ventilation. We extracted data independently using a standardized data extraction tool, and feedback was received from all co-authors to ensure agreement. For each outcome, we performed meta-analyses using a random-effects model with inverse variance weighting in RevMan. We used the GRADE approach to assess certainty in pooled estimates of effect. Of 1325 screened articles, we found 4 that fit our inclusion criteria. Together, we judged risk of bias as 'some concerns' in 3 trials and 'high' in the final trial, and ultimately found that the effects of anabolic-androgen therapy on patient-important outcomes uncertain. With the uncertainty of current evidence for the effects of anabolic-androgen therapy in critically ill adults, there is insufficient support for its routine use. Future randomized evidence is needed to determine whether anabolic-androgen therapy improves clinically-important outcomes and better define its safety profile in critically ill adults.

Humans↗

PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗

PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗

PubMed2026

Efficacy and Safety of Mechanical Insufflation-Exsufflation in Invasively Ventilated Critically Ill Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Mechanical insufflation-exsufflation (MI-E) is increasingly used in invasively ventilated adults in the intensive care unit (ICU), yet its therapeutic efficacy and safety remain uncertain due to inconsistent evidence. AIM: To synthesize evidence on the clinical efficacy and safety of MI-E in this population and to examine methodological and clinical heterogeneity underlying reported outcomes. STUDY DESIGN: A systematic review and meta-analysis of randomized studies (including RCTs and randomized crossover trials), conducted following PRISMA guidelines, with risk of bias assessed using the Cochrane risk-of-bias tool. RESULTS: Five randomized controlled trials involving 310 patients were included. Meta-analysis showed that mechanical insufflation-exsufflation (MI-E) significantly increased sputum clearance (SMD = 0.63, 95% CI, 0.32-0.93; p < 0.00011; I2 = 38%) without affecting oxygenation (MD = 0.28, 95% CI, -0.53 to 1.09; p = 0.50; I2 = 9%). Data on respiratory mechanics, ventilation duration and ICU stay could not be pooled. No serious adverse events were reported. CONCLUSIONS: MI-E significantly improves sputum clearance in invasively ventilated critically ill adults, with no severe adverse events reported in the included studies. Its effects on other outcomes remain inconclusive due to limited data and heterogeneity. Standardized protocols and larger trials are needed. RELEVANCE TO CLINICAL PRACTICE: Clinicians may consider MI-E as an adjunct for respiratory secretion management. Application should be guided by structured patient assessment and individualized parameter adjustment. Future research should standardize interventions and target well-defined patient subgroups to inform clear practice guidelines. TRIAL REGISTRATION: The review protocol was registered in the International Prospective Register of Systematic Reviews, with registration number CRD42023403299.

Humans↗

PubMed2026

Risk factors of venous thromboembolism in ICU patients: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to identify risk factors associated with the development of VTE in patients admitted to the intensive care unit (ICU). METHODS: A systematic literature search was conducted via PubMed, Embase, Web of Science, and Cochrane databases up to 25 April 2025, to identify studies examining the association between risk factors and the occurrence of venous thromboembolism (VTE) in ICU patients. Data were pooled using odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: A total of 2465 relevant studies were identified through the systematic search, of which 30 were included in the meta-analysis. The pooled data showed that the following were significant risk factors for venous thromboembolism (VTE) in ICU patients: central venous catheterization (OR = 2.67, 95% CI: 1.67-4.28; I2 = 28%), invasive mechanical ventilation (OR = 2.08, 95% CI: 1.46-2.96; I2 = 0%), advanced age (OR = 2.06, 95% CI: 1.28-3.31; I2 = 86%), length of ICU stay (OR = 4.24, 95% CI: 1.43-12.57; I2 = 98%), malignancy (OR = 2.30, 95% CI: 1.03-5.12; I2 = 67%), elevated D-dimer levels (OR = 2.46, 95% CI: 1.37-4.40; I2 = 34%), and a history of VTE (OR = 2.84, 95% CI: 1.45-5.55; I2 = 51%). According to the GRADE assessment, the quality of evidence was rated as moderate for invasive mechanical ventilation, low for central venous catheterization and D-dimer levels, and very low for the remaining factors. CONCLUSION: Invasive mechanical ventilation, central venous catheterization, and elevated D-dimer levels are associated with VTE risk, supported by relatively high-quality evidence. These findings may help identify ICU patients at higher risk of VTE, inform the development of risk assessment models for patient stratification, and ultimately contribute to improved prognosis through optimal screening and management strategies.

Humans↗

PubMed2026

Comparison of VCV and PCV-VG modes on diaphragmatic function in diabetic patients undergoing laparoscopic colorectal surgery: a prospective randomized controlled study.

BACKGROUND: Diabetic patients are prone to induce diaphragmatic weakness, which can lead to postoperative pulmonary complications (PPCs). The optimal mechanical ventilation mode may potentially improve postoperative diaphragmatic function. This study evaluates the effects of two ventilation modes under driving pressure-guided ventilation strategy on diaphragmatic function, as assessed by diaphragm thickening fraction (DTF) and diaphragm excursion (DE), in diabetic patients following laparoscopic colorectal surgery. METHODS: Eighty patients diagnosed with Type II diabetes scheduled for elective laparoscopic colorectal surgery, were randomly allocated to either the pressure-controlled volume-guaranteed ventilation (PCV-VG) group (Group P) or the volume-controlled ventilation (VCV) group (Group V) during surgery. The primary outcome was diaphragmatic function assessed during both tidal breathing and maximal inspiratory effort after surgery. Secondary outcomes included intraoperative mechanical power, PPCs, and other complications. RESULTS: A total of eighty patients were included in the final analysis. The averaged area under the curve (AUC) for mechanical power during ventilation was significantly lower in Group P than in Group V (p = 0.002). PCV-VG significantly improved both DE and DTF within the first two days post-surgery (AUCDEtidal: p = 0.088, AUCDTFtidal: p = 0.004, AUCDEmax: p = 0.029, AUCDTFmax: p = 0.017). Postoperative diaphragmatic weakness was less frequent in Group P than in Group V (p = 0.019). However, there was no difference in the incidence of PPCs between the two groups (p = 0.155). CONCLUSION: PCV-VG mode can reduce intraoperative mechanical power, better preserve postoperative diaphragmatic function. However, these improvements did not translate into clinical benefits, as evidenced by the lack of reduction in the incidence of PPCs.

Humans↗

PubMed2026

Feasibility and efficacy of left bundle branch area pacing guided by modified chest lead 1.

BACKGROUND: Left bundle branch area pacing (LBBAP) typically requires 12‑lead electrocardiogram (ECG) measurements using an electrophysiology (EP) recording system. However, a simplified approach using modified chest lead 1 (MCL1) is potentially feasible. This study aimed to compare the success rate and pacing outcomes of LBBAP guided by MCL1 with those guided by the 12‑lead ECG using an EP recording system. METHODS: This retrospective, single-center study included patients with preserved left ventricular ejection fraction who underwent LBBAP for bradyarrhythmia. LBBAP was either guided by 12‑lead ECG using an EP recording system or by MCL1. In the MCL1 group, a follow-up examination with a 12‑lead ECG using an EP recording system was conducted within one week postoperatively. RESULTS: A total of 65 patients underwent LBBAP (EP recording system group: n = 35; MCL1 group: n = 30). The overall success rate of LBBAP was 84.6%, with no significant difference between groups (88.5% vs. 80.0%, p = 0.49). No significant differences were observed in the paced QRS duration (140.4 ± 8.0 vs. 141.9 ± 13.1 ms, p = 0.54), V6-V1 interpeak interval (39.7 ± 16.5 vs. 38.3 ± 15.6 ms, p = 0.79), or V6 R-wave peak time (69.8 ± 12.3 vs. 71.5 ± 12.1 ms, p = 0.68). CONCLUSIONS: MCL1-guided LBBAP was feasible and achieved a high success rate, with outcomes comparable to those of conventional EP recording system-guided implantation. This simplified approach may reduce procedural complexity and may allow LBBAP implantation without the routine use of an EP recording system.

Humans↗