Lancet Digit Health. 2025 Aug 5:100894. doi: 10.1016/j.landig.2025.100894. Online ahead of print.

ABSTRACT

BACKGROUND: Sexually transmitted infections (STIs) are a substantial public health concern. We aimed to evaluate the accuracy and applicability of deep learning algorithms in the early detection of STIs from skin lesions.

METHODS: In this systematic review and meta-analysis, we searched PubMed, Institute of Electrical and Electronics Engineers Xplore, Web of Science, Scopus for studies employing deep learning for classifying clinical skin lesion images of STIs published between Jan 1, 2010, and Dec 31, 2023. Studies that did not include clinical images were excluded. The primary outcome was diagnostic performance, assessed by pooled sensitivity and specificity. We conducted a meta-analysis of the studies providing contingency tables using a unified hierarchical model. We additionally assessed the quality of the studies using modified QUADAS-2 and CheckList for Evaluation of image-based AI Reports in Dermatology (CLEAR Derm) criteria. This study was registered with PROSPERO, CRD42024496966.

FINDINGS: Among the 1946 studies identified, we included 101 in our review. The majority of the included studies focused on mpox (91 [88%] of 101 studies), followed by scabies (eight [8%] studies), herpes (four [4%] studies), syphilis (one [1%] study), and molluscum (one [1%] study). A meta-analysis of 55 studies showed that deep learning algorithms had a pooled sensitivity of 0·97 (95% CI 0·95-0·98) and a specificity of 0·99 (0·98-0·99) for mpox, and a sensitivity of 0·95 (0·90-0·98) and specificity of 0·97 (0·86-0·99) for scabies. The majority of studies (86 [85%] of 101 studies) utilised public datasets; traditional convolutional neural networks with backbone architectures such as ResNet and VGGNet were used in all studies. However, notable quality issues related to the data, technical descriptions of labelling methods and diagnostic label references, technical assessment for public evaluation of algorithms, benchmarking and bias assessments, application descriptions of use cases, and target conditions and potential impacts were identified in CLEAR Derm. Potential biases in performance evaluation metrics and applicability concerns in the data, deep learning algorithms, and performance evaluation metrics might impede the generalisability of these models to real-world clinical practice and STI screening across diverse populations.

INTERPRETATION: Although deep learning shows potential for early detection of STIs, there are challenges to ensuring the generalisability of such algorithms due to limited heterogeneous data. Standardised, diverse skin lesion image datasets are crucial to ensure fair comparisons and reliable performance.

FUNDING: City University of Hong Kong.

PMID:40769792 | DOI:10.1016/j.landig.2025.100894

Int J Pharm. 2025 Aug 4:126024. doi: 10.1016/j.ijpharm.2025.126024. Online ahead of print.

ABSTRACT

Prostate cancer (PCa) represents one of the most prevalent cancers among men, with substantial challenges in timely and accurate diagnosis and subsequent treatment. Traditional diagnosis and treatment methods for PCa, such as prostate-specific antigen (PSA) biomarker detection, digital rectal examination, imaging (CT/MRI) analysis, and biopsy histopathological examination, suffer from limitations such as a lack of specificity, generation of false positives or negatives, and difficulty in handling large data, leading to overdiagnosis and overtreatment. The integration of artificial intelligence (AI) in PCa diagnosis and treatment is revolutionizing traditional approaches by offering advanced tools for early detection, personalized treatment planning, and patient management. AI technologies, especially machine learning and deep learning, improve diagnostic accuracy and treatment planning. The AI algorithms analyze imaging data, like MRI and ultrasound, to identify cancerous lesions effectively with great precision. In addition, AI algorithms enhance risk assessment and prognosis by combining clinical, genomic, and imaging data. This leads to more tailored treatment strategies, enabling informed decisions about active surveillance, surgery, or new therapies, thereby improving quality of life while reducing unnecessary diagnoses and treatments. This review examines current AI applications in PCa care, focusing on their transformative impact on diagnosis and treatment planning while recognizing potential challenges. It also outlines expected improvements in diagnosis through AI-integrated systems and decision support tools for healthcare teams. The findings highlight AI's potential to enhance clinical outcomes, operational efficiency, and patient-centred care in managing PCa.

PMID:40769449 | DOI:10.1016/j.ijpharm.2025.126024

JMIR Med Inform. 2025 Aug 6;13:e73631. doi: 10.2196/73631.

ABSTRACT

BACKGROUND: Seasonal influenza is a major global public health concern, leading to escalated morbidity and mortality rates. Traditional early warning models rely on binary (0/1) classification methods, which issue alerts only when predefined thresholds are crossed. However, these models exhibit inflexibility, often leading to false alarms or missed warnings and failing to provide granular risk assessments essential for decision-making. Therefore, we propose a probability-based early warning system using machine learning to mitigate these limitations and to offer continuous risk estimations of alerts (0-1 variable) instead of rigid threshold-based alerts. Based on probabilistic prediction, public health experts can make more flexible decisions in combination with the actual situation, significantly reducing the uncertainty and pressure in the decision-making process and reducing the waste of public health resources and the risk of social panic.

OBJECTIVE: The main aim of this study is to devise an innovative approach for early warning systems focused on influenza-like cases. Therefore, a Dense Residual Network (Dense ResNet), a supervised deep learning model, was developed. The model's training involved fitting the influenza-like illness positive rate, which enabled the early detection and warning of signals of changes occurring in the activity level of influenza-like cases. This departure from conventional methodologies underscores the transformative potential of machine learning, particularly in providing advanced capabilities for timely and proactive warnings in the context of influenza outbreaks.

METHODS: We developed a Dense ResNet machine learning model trained on influenza surveillance data from Northern and Southern China (2014-2024). This model generates early warning signals 3, 5, and 7 days in advance, providing a probability-based risk assessment represented as a continuous variable ranging from 0 to 1, in contrast to the traditional binary (0/1) warning systems. We evaluated the performance of this model using area under the curve scores, accuracy, recall, and F1-scores, then compared it with support vector machine (SVM), random forests, XGBoost (Extreme Gradient Boosting), and LSTM (long short-term memory) models.

RESULTS: The Dense ResNet model demonstrated the best performance, characterized by 5-day lead warnings and a 50th percentile probability threshold, achieving area under the curve scores of 0.94 (Northern China) and 0.95 (Southern China). Relative to traditional models, probability-based warning signals improved early detection, reduced false alarms, and facilitated tiered public health responses.

CONCLUSIONS: This study presented a novel probability-based machine learning model essential for early warning signals of influenza, demonstrating superior accuracy, flexibility, and practical applicability compared to other techniques. This approach enhances preparedness for influenza among the population and promotes the use of automated artificial intelligence-driven public health responses by replacing binary warnings with probability-driven risk assessments. Future research should integrate real-time surveillance data and dynamic transmission models to improve the precision of early warning.

PMID:40769217 | DOI:10.2196/73631

J Neural Eng. 2025 Aug 6. doi: 10.1088/1741-2552/adf888. Online ahead of print.

ABSTRACT

Surface electromyography (sEMG) and pressure-based force myography (pFMG) are two complementary modalities adopted in hand gesture recognition due to their ability to capture muscle electrical and mechanical activity, respectively. While sEMG carries rich neural information about the intended gestures and has long been established as the primary control signal in myoelectric interfaces, pFMG has recently emerged as a stable modality that is less sensitive to sweat and can indicate motion onset earlier than sEMG, making their fusion promising for robust pattern recognition. However, gesture classification systems based on these signals often suffer from performance degradation due to limb position changes, which affect signal characteristics. To address this, we introduce MyoPose, a novel and lightweight spatial synergy-based feature set for enhancing neuromechanical control. MyoPose works on effectively decoding colocated sEMG-pFMG information to improve hand gesture recognition under limb position variability while remaining computationally efficient for resource-constrained hardware. The proposed MyoPose feature combined with Linear Discriminant Analysis (LDA), achieved 87.7% accuracy in a nine-hand gesture recognition task, outperforming standard myoelectric feature sets and comparable to a state-of-the-art decision-level multimodal fusion parallel CNN. Notably, MyoPose maintained computational efficiency, achieving real-time feasibility with an estimated controller delay of 110.62 ms, well within the operational requirement of 100-125 ms, as well as ultra-light memory requirement of 0.011 KB. The novelty of this study lies in providing an effective feature set for multimodal driven hand gesture recognition, handling limb position variations with robust accuracy, and showing potential for real-time feasibility for human-machine interfaces without the need for deep learning.

PMID:40769169 | DOI:10.1088/1741-2552/adf888

Med Image Anal. 2025 Jul 29;106:103715. doi: 10.1016/j.media.2025.103715. Online ahead of print.

ABSTRACT

In pediatric orthodontics, accurate estimation of growth potential is essential for developing effective treatment strategies. Our research aims to predict this potential by identifying the growth peak and analyzing cervical vertebra morphology solely through lateral cephalometric radiographs. We accomplish this by comprehensively analyzing cervical vertebral maturation (CVM) features from these radiographs. This methodology provides clinicians with a reliable and efficient tool to determine the optimal timings for orthodontic interventions, ultimately enhancing patient outcomes. A crucial aspect of this approach is the meticulous annotation of keypoints on the cervical vertebrae, a task often challenged by its labor-intensive nature. To mitigate this, we introduce Attend-and-Refine Network (ARNet), a user-interactive, deep learning-based model designed to streamline the annotation process. ARNet features Interaction-guided recalibration network, which adaptively recalibrates image features in response to user feedback, coupled with a morphology-aware loss function that preserves the structural consistency of keypoints. This novel approach substantially reduces manual effort in keypoint identification, thereby enhancing the efficiency and accuracy of the process. Extensively validated across various datasets, ARNet demonstrates remarkable performance and exhibits wide-ranging applicability in medical imaging. In conclusion, our research offers an effective AI-assisted diagnostic tool for assessing growth potential in pediatric orthodontics, marking a significant advancement in the field.

PMID:40769097 | DOI:10.1016/j.media.2025.103715

Microb Pathog. 2025 Aug 4:107949. doi: 10.1016/j.micpath.2025.107949. Online ahead of print.

ABSTRACT

Pulmonary tuberculosis (PTB) and gastroesophageal reflux disease (GERD) are clinically distinct but may share common molecular mechanisms. This study used integrative bioinformatics to identify 23 significantly dysregulated genes common to both conditions. Key regulatory microRNAs (hsa-miR-34a-5p, hsa-let-7b-5p, hsa-let-7g-5p) and macrophage-associated pathways were implicated. Interferon alpha/beta signaling emerged as a central shared pathway between PTB and GERD. Five hub genes (MYL9, OASL, ACTA2, DDX60L, and DDX60) were identified as common between these two conditions and their expressions were validated using quantitative real-time PCR. Drug repurposing analysis identified ribavirin as a promising candidate targeting the hub gene OASL, supported by a favorable binding affinity (-6.6 kcal/mol) and acceptable ADMET properties. These findings pave the way for understanding the molecular overlap between PTB and GERD and for developing targeted therapeutic strategies.

PMID:40769224 | DOI:10.1016/j.micpath.2025.107949

Nat Commun. 2025 Aug 7;16(1):7267. doi: 10.1038/s41467-025-61712-2.

ABSTRACT

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform in-depth analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development. The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale case-based diagnostic analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser ( https://dbmi-bgm.github.io/udn-browser/ ). These results show that case-level diagnostic efforts should be supplemented by a joint genomic analysis across cohorts.

PMID:40770127 | DOI:10.1038/s41467-025-61712-2

Br J Anaesth. 2025 Aug 5:S0007-0912(25)00350-2. doi: 10.1016/j.bja.2025.05.036. Online ahead of print.

ABSTRACT

INTRODUCTION: Both the dose requirements and side-effects of propofol vary significantly between individuals. Propofol is primarily metabolised by uridine 5'-diphosphate-glucuronosyltransferase (UGT) and cytochrome P450 (CYP) enzymatic pathways, specifically UGT1A9 (70%) and CYP2B6/CYP2C9 (29%). Genetic polymorphisms, which influence enzyme transcription and function, can influence propofol metabolism and propofol clearance and thereby potentially impact anaesthesia-related clinical outcomes. Pharmacogenomic testing can assist with individualising propofol dosing during total intravenous anaesthesia (TIVA) to improve safety and efficacy. This systematic review evaluates the impact of metabolic genetic polymorphisms on propofol clearance, dose requirements, and related clinical effects.

METHODS: A systematic search of MEDLINE, EMBASE, and the Pharmacogenomics Knowledge Base identified studies involving patients who underwent propofol-based TIVA and pharmacogenomic testing for UGT1A9, CYP2B6, and CYP2C9 genotypes.

RESULTS: Sixteen studies, involving 1779 patients receiving propofol-TIVA, were included. Of the UGT1A9 polymorphism genotypes considered, CT heterozygotes of rs72551330 (98T>C) may have a clinically relevant effect on propofol pharmacokinetics, with lower propofol clearance, lower dose requirements, and longer emergence times. CC homozygotes of rs2741045 (-440C>T) may have higher propofol clearance with faster emergence. Given currently available data, the CYP2B6 and CYP2C9 genotypes do not appear to have significant influence on propofol pharmacokinetics or anaesthesia-related clinical outcomes.

CONCLUSIONS: Genetic polymorphisms in propofol metabolism can influence the pharmacokinetics of propofol and, potentially, anaesthesia-related clinical outcomes. To confirm these observations, larger, well-designed pharmacokinetic studies exploring metabolic genetic polymorphisms as covariates are required. Such data could support pharmacogenomic-guided propofol dosing.

PMID:40769841 | DOI:10.1016/j.bja.2025.05.036

Value Health. 2025 Aug 4:S1098-3015(25)02485-4. doi: 10.1016/j.jval.2025.07.018. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the cost-effectiveness of pre-emptive CYP2D6 and CYP2C19 genotype-informed tricyclic antidepressant dosing from a societal and a healthcare perspective.

METHODS: A trial-based cost-effectiveness analysis was conducted with data from the Pharmacogenetics for Individualized Tricyclic Antidepressant dosing (PITA) study. This multicenter randomized controlled trial (n=111) compared pharmacogenetic-informed treatment (PIT) with treatment-as-usual (TAU). Quality-adjusted life years (QALY) and costs were measured at 13 and 26 weeks. Prices were based on or indexed to 2022 tariffs. Single imputation nested in the bootstrap percentile method (using 5000 bootstrap replications) was performed to address missing data and to estimate uncertainty around cost-effectiveness outcomes. Incremental Net Monetary Benefit (iNMB) was calculated based on a willingness-to-pay (WTP) of €50.000/QALY.

RESULTS: Our data showed a marginal difference of -0.0125 QALYs (95%-confidence interval (CI) -0.0404 to 0.0149, week 13) and 0.0012 QALYs (95%-CI -0.0517 to 0.0491, week 26) for PIT versus TAU. From the healthcare perspective, a cost saving of €148 (week 13) and €521 (26 week) was found for PIT. The societal perspective showed increased costs of €1300 (week 13) and €1704 (26 weeks) for PIT. The mean iNMB from a healthcare perspective was positive at 26 weeks, the other iNMB (13 weeks and societal perspective) were negative.

CONCLUSIONS: We observed marginal differences of QALYs in both the healthcare and societal perspective with cost savings from the healthcare perspective and additional cost from the societal perspective. These mixed results warrant more long-term observational studies to determine whether pre-emptive genotyping in tricyclic antidepressant dosing will be cost-effective.

PMID:40769295 | DOI:10.1016/j.jval.2025.07.018

Clin Ther. 2025 Aug 5:S0149-2918(25)00244-9. doi: 10.1016/j.clinthera.2025.07.008. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to evaluate the effects of azithromycin on lung function in children with cystic fibrosis (CF) through a systematic review and meta-analysis of randomized controlled trials (RCTs). The study primarily focuses on its impact on FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity), and the progression of lung function decline.

METHODS: Electronic searches were conducted across PubMed,Cochrane Central, Embase, Web of Science, and China National Knowledge Infrastructure databases, including studies published up to November 1, 2024. Inclusion criteria required RCTs involving children with CF, azithromycin as the intervention, and placebo controls. Meta-analyses were performed using random-effects models, and heterogeneity was assessed using the I² statistic. Sensitivity analyses were conducted to ensure the robustness of results.

FINDINGS: Eight RCTs were included, covering a total of 625 participants. Meta-analysis revealed that azithromycin significantly improved FEV1 compared to the control group, with a standardized mean difference (SMD) of 0.58 (95% CI: 0.03-1.14), though substantial heterogeneity was observed (I² = 82.8%). However, no statistically significant improvement in FVC was detected (SMD: 0.62, 95% CI: -0.04 to 1.29, I² = 85.4%). Additionally, azithromycin reduced the relative risk of lung function decline (RR: 0.79, 95% CI: 0.62-1.00), with moderate heterogeneity (I² = 45.5%). Sensitivity analyses confirmed the stability of these results.

IMPLICATIONS: Azithromycin shows potential in improving FEV1 and slowing lung function decline in children with cystic fibrosis, likely through its anti-inflammatory and immunomodulatory effects. Further large-scale studies are warranted to confirm its long-term efficacy, evaluate safety, and optimize treatment strategies, including potential combination therapies.

PMID:40769868 | DOI:10.1016/j.clinthera.2025.07.008

Clin Chest Med. 2025 Sep;46(3):559-567. doi: 10.1016/j.ccm.2025.04.014. Epub 2025 Jul 1.

ABSTRACT

This article provides an overview of remote or home spirometry. It includes discussions about the types of devices available, their accuracy, and pitfalls. It also summarizes the data available for the use of home spirometry in specific pulmonary diseases such as lung transplant, cystic fibrosis, neuromuscular disease, and obstructive lung disease.

PMID:40769599 | DOI:10.1016/j.ccm.2025.04.014

Eur Respir Rev. 2025 Aug 6;34(177):240255. doi: 10.1183/16000617.0255-2024. Print 2025 Jun.

ABSTRACT

Great strides have been made in pre-clinical research in recent decades using animal models and cell lines. However, traditional models may fail to translate to humans, resulting in substantial failure rates in drug development. Recent three-dimensional organoid models have borne a good resemblance to the architecture, development and function of tissues, especially for organs with complex cell interactions and dynamics such as the lungs. In 2022, the role of organoids as alternative to animal testing was recognised by the US Food and Drug Administration. We searched Medline and ClinicalTrials.gov for studies on the experimental use of lung organoids to model disease pathogenesis and test treatments for paediatric and neonatal respiratory diseases. We comprehensively review the translational value of organoids for paediatric and neonatal respiratory conditions, with current limitations and future expectations, while glancing at other in vitro respiratory models. Combinations of organoid models varying in derivation and differentiation have been used to test interventions for conditions such as infectious/inflammatory diseases, abnormalities of the lung vasculature, surfactant deficiency and genetic diseases. Even multifactorial diseases such as congenital diaphragmatic hernia and bronchopulmonary dysplasia are benefiting from new options for patient-specific sampling and organoid derivation. Microscale technologies and engineering contribute to further advancements in lung-on-chip and microfluidic environments. Overall, organoids show great potential as a bridge between basic research and clinical applications, with versatile adaptability to research purposes. Patient-derived organoids carry exciting possibilities for both personalised medicine and clinical research. Rapid advances in regenerative medicine and engineering have opened up new avenues for neonatology and paediatric respiratory medicine.

PMID:40769535 | DOI:10.1183/16000617.0255-2024

Cell Host Microbe. 2025 Jul 31:S1931-3128(25)00281-1. doi: 10.1016/j.chom.2025.07.009. Online ahead of print.

ABSTRACT

Cystic fibrosis transmembrane conductance regulator (CFTR) modulators improve the physiological defect causing cystic fibrosis, but the lungs of most people remain infected and inflamed. A leading hypothesis implicates damaged segments as the cause of persistent infection and predicts that mildly diseased segments within an individual's lungs will clear after treatment, whereas severely diseased segments will not. Our findings contradict this hypothesis. We used bronchoscopy to sample the least- and most-damaged lung segments in Pseudomonas aeruginosa (Pa)-infected individuals before modulators and returned to these same segments after 1.5 years. Surprisingly, we find an "all-or-none" infection clearance response: the most-diseased segments clear if any other lung segment in that person clears, and the least-diseased segments remain infected if others in that person do. Furthermore, neutrophilic inflammation completely resolves where Pa clears but remains elevated where Pa persists. These data indicate that post-modulator infections are not limited to severely diseased segments and that Pa infections drive persistent lung inflammation after modulators.

PMID:40769150 | DOI:10.1016/j.chom.2025.07.009

Sci Rep. 2025 Aug 6;15(1):28804. doi: 10.1038/s41598-025-05574-0.

ABSTRACT

Steel defect detection is a crucial aspect of steel production and quality control. Therefore, focusing on small-scale defects in complex production environments remains a critical challenge. To address this issue, we propose an innovative perception-efficient network designed for the fast and accurate detection of multi-scale surface defects. First, we introduce the Defect Capture Path Aggregation Network, which enhances the feature fusion network's ability to learn multi-scale representations. Second, we design a Perception-Efficient Head (PEHead) to effectively mitigate local aliasing issues, thereby reducing the occurrence of missed detections. Finally, we propose the Receptive Field Extension Module (RFEM) to strengthen the backbone network's ability to capture global features and address extreme aspect ratio variations. These three modules can be seamlessly integrated into the YOLO framework. The proposed method is evaluated on three public steel defect datasets: NEU-DET, GC10-DET, and Severstal. Compared to the original YOLOv8n model, PEYOLO achieves mAP50 improvements of 3.5%, 9.1%, and 3.3% on these datasets, respectively. While maintaining similar detection accuracy, PEYOLO retains a high inference speed, making it suitable for real-time applications. Experimental results demonstrate that the proposed PEYOLO can be effectively applied to real-time steel defect detection.

PMID:40770273 | DOI:10.1038/s41598-025-05574-0

Sci Data. 2025 Aug 6;12(1):1366. doi: 10.1038/s41597-025-05718-x.

ABSTRACT

Artificial intelligence advancements have significantly enhanced computer-aided intervention, learning among surgeons, and analysis of surgical videos post-operation, substantially elevating surgical expertise and patient outcomes. Recognition systems for endoscopic surgical phases using deep learning algorithms heavily rely on comprehensive annotated datasets. Our research presents the Renji dataset featuring videos of endoscopic submucosal dissection (ESD) for colorectal neoplastic lesions (CNLs), which includes 30 procedural recordings with 130,298 phase-specific annotations collaboratively labeled by a team of three specialists in endoscopy. To our knowledge, this represents the first openly accessible collection of ESD videos specifically targeting CNLs treatment, and we anticipate this work will help establish standards for constructing similar ESD databases. Both the video collection and corresponding annotations have been made publicly accessible through the Figshare platform.

PMID:40770268 | DOI:10.1038/s41597-025-05718-x

Sci Rep. 2025 Aug 6;15(1):28814. doi: 10.1038/s41598-025-09739-9.

ABSTRACT

Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle study, a deep learning approach to multi-material decomposition was developed to quantify iodine, gadolinium, and calcium in spectral CT. A dual-phase network architecture was trained using synthetic datasets containing computational models of cylindrical and virtual patient phantoms. Classification and quantification performance was evaluated across a range of patient size and dose parameters. The model was found to accurately classify (accuracy: cylinders - 98%, virtual patients - 97%) and quantify materials (mean absolute percentage difference: cylinders - 8-10%, virtual patients - 10-15%) in both datasets. Performance in virtual patient phantoms improved as the hybrid training dataset included a larger contingent of virtual patient phantoms (accuracy: 48% with 0 virtual patients to 97% with 8 virtual patients). For both datasets, the algorithm was able to maintain strong performance under challenging conditions of large patient size and reduced dose. This study shows the validity of a deep-learning based approach to multi-material decomposition trained with in-silico images that can overcome the limitations of conventional material decomposition approaches.

PMID:40770244 | DOI:10.1038/s41598-025-09739-9

Sci Rep. 2025 Aug 6;15(1):28787. doi: 10.1038/s41598-025-12679-z.

ABSTRACT

Optimizing clinical pathways is pivotal for enhancing healthcare delivery, yet traditional methods are increasingly insufficient in the face of complex, personalized medical demands. This paper introduces an innovative optimization framework that fuses Latent Dirichlet Allocation (LDA) topic modeling with Bidirectional Long Short-Term Memory (BiLSTM) networks to address the complexities of modern healthcare. The LDA component elucidates key diagnostic and treatment patterns from clinical narratives, while the BiLSTM network adeptly captures the temporal progression of patient care. Our model was validated against a real-world medical dataset, achieving remarkable results with an accuracy of over 90%, precision exceeding 28% improvement, recall with a 21% enhancement, and an F1 score that reflects a 25% increase over existing models. These results were obtained through comparative analysis with established models such as DeepCare, Doctor AI, and LSTM variants, showcasing the superior predictive capabilities of our LDA-BiLSTM integrated approach. This study not only advances the academic discourse on clinical pathway management but also presents a tangible tool for healthcare practitioners, promising a significant impact on the customization and efficacy of clinical pathways, thereby enhancing patient care and satisfaction.

PMID:40770243 | DOI:10.1038/s41598-025-12679-z

Sci Data. 2025 Aug 6;12(1):1369. doi: 10.1038/s41597-025-05708-z.

ABSTRACT

Deep learning models can accelerate the processing of image-based biodiversity data and provide educational value by giving direct feedback to citizen scientists. However, the training of such models requires large amounts of labelled data and not all species are equally suited for identification from images alone. Most butterfly and many moth species (Lepidoptera) which play an important role as biodiversity indicators are well-suited for such approaches. This dataset contains over 540.000 images of 185 butterfly and moth species that occur in Austria. Images were collected by citizen scientists with the application "Schmetterlinge Österreichs" and correct species identification was ensured by an experienced entomologist. The number of images per species ranges from one to nearly 30.000. Such a strong class imbalance is common in datasets of species records. The dataset is larger than other published dataset of butterfly and moth images and offers opportunities for the training and evaluation of machine learning models on the fine-grained classification task of species identification.

PMID:40770239 | DOI:10.1038/s41597-025-05708-z

Sci Rep. 2025 Aug 6;15(1):28723. doi: 10.1038/s41598-025-14406-0.

ABSTRACT

Alzheimer's disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transition time from Cognitive Normal (CN) to Mild Cognitive Impairment (MCI) in elderly individuals, considering the predictive value of baseline comorbidities. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) databases, we construct feature sets encompassing demographics, cognitive scores, and comorbidities. Various machine learning and deep learning methods for survival analysis are employed. Our top-performing model, fast random forest, achieves a concordance index of 0.84 when considering all feature modalities, with comorbidity data emerging as a significant predictor. The top features identified by the best-performing model include one demographic feature (age), seven cognitive scores (ADAS13, RAVLT learning, FAQ, ADAS11, RAVLT immediate, CDRSB, ADASQ4), and two comorbidities (Endocrine & Metabolic, Renal & Genitourinary). Age is highlighted as the most influential predictor, while cognitive scores are crucial indicators of Alzheimer's disease. External validation against the AIBL dataset affirms the robustness of our approach. Overall, our study contributes to a deeper understanding of the role of baseline comorbidities in AD risk prediction and emphasizes the importance of incorporating comprehensive feature assessment in clinical practice for early diagnosis and personalized treatment planning.

PMID:40770222 | DOI:10.1038/s41598-025-14406-0

Eur Radiol. 2025 Aug 6. doi: 10.1007/s00330-025-11801-z. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to evaluate the potential additional value of deep radiomics for assessing residual cancer burden (RCB) in locally advanced breast cancer, after neoadjuvant chemotherapy (NAC) but before surgery, compared to standard predictors: tumor volume and subtype.

MATERIALS AND METHODS: This retrospective study used a 105-patient single-institution training set and a 41-patient external test set from three institutions in the LIMA trial. DCE-MRI was performed before and after NAC, and RCB was determined post-surgery. Three networks (nnU-Net, Attention U-net and vector-quantized encoder-decoder) were trained for tumor segmentation. For each network, deep features were extracted from the bottleneck layer and used to train random forest regression models to predict RCB score. Models were compared to (1) a model trained on tumor volume and (2) a model combining tumor volume and subtype. The potential complementary performance of combining deep radiomics with a clinical-radiological model was assessed. From the predicted RCB score, three metrics were calculated: area under the curve (AUC) for categories RCB-0/RCB-I versus RCB-II/III, pathological complete response (pCR) versus non-pCR, and Spearman's correlation.

RESULTS: Deep radiomics models had an AUC between 0.68-0.74 for pCR and 0.68-0.79 for RCB, while the volume-only model had an AUC of 0.74 and 0.70 for pCR and RCB, respectively. Spearman's correlation varied from 0.45-0.51 (deep radiomics) to 0.53 (combined model). No statistical difference between models was observed.

CONCLUSIONS: Segmentation network-derived deep radiomics contain similar information to tumor volume and subtype for inferring pCR and RCB after NAC, but do not complement standard clinical predictors in the LIMA trial.

KEY POINTS: Question It is unknown if and which deep radiomics approach is most suitable to extract relevant features to assess neoadjuvant chemotherapy response on breast MRI. Findings Radiomic features extracted from deep-learning networks yield similar results in predicting neoadjuvant chemotherapy response as tumor volume and subtype in the LIMA study. However, they do not provide complementary information. Clinical relevance For predicting response to neoadjuvant chemotherapy in breast cancer patients, tumor volume on MRI and subtype remain important predictors of treatment outcome; deep radiomics might be an alternative when determining tumor volume and/or subtype is not feasible.

PMID:40770139 | DOI:10.1007/s00330-025-11801-z