Diagnostic Model Research Articles

Saliva NIR spectroscopy and Aquaphotomics: a novel diagnostic approach to Paratuberculosis in dairy cattle

IntroductionParatuberculosis is a granulomatous intestinal infection that affects ruminant animals worldwide. The disease is often detected when most animals are already infected due to the long incubation period and the high transmissibility of the infectious agent. The lack of a comprehensive method to diagnose Paratuberculosis is a global challenge. Therefore, a non-destructive, fast, and cost-effective diagnostic method for early detection of Paratuberculosis is crucial.MethodsNear-infrared spectroscopy (NIRS) and Aquaphotomics have the potential to diagnose the disease by detecting changes in biological fluids. This study aimed to investigate the diagnostic ability of NIRS and Aquaphotomics for Paratuberculosis in dairy cattle by monitoring and data mining of saliva. The diagnostic models were developed according to saliva spectra of dairy cattle in the NIR range and 12 water absorbance bands from 100 to 200 days after calving in two groups: positive and negative, based on the same results of seven ELISA tests of blood plasma, as a reference test.ResultsBoth NIRS and Aquaphotomics methods had high diagnostic accuracy. Using QDA and SVM models, 99% total accuracy, 98% sensitivity, and 100% specificity were achieved in internal validation. The total accuracy in external validation was 90%. This study presents two novel approaches to diagnosing Paratuberculosis in dairy cattle using saliva.DiscussionThe study found that changes in water absorbance spectral patterns of saliva caused by complex physiological changes, such as the amount of antibody related to Paratuberculosis in dairy cattle as biomarkers, are crucial in detecting Paratuberculosis using NIRS and Aquaphotomics.

Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward

Abstract Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Toward this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumor visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumors that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep-learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions and prognostic evaluations for TNBC.

BN SO24 - Predicting incidental gallbladder cancer: A protocol for identifying predictive factors of incidentally detected gallbladder cancer and prospective validation of a scoring system to allow selective histological analysis of the gallbladder

Abstract Background Incidental Gallbladder Cancer (iGBC) is a rare complication of cholecystectomy, with a UK incidence of 0.14-0.3%. Despite its rarity, most gallbladder specimens are analysed histologically. Some comparable countries undertake Selective Histological Analysis (SHA), selecting high-risk specimens for histology based on clinical judgement or postoperative inspection. However, evidence suggests a third of diagnoses can be missed with these approaches. Swedish and Dutch diagnostic scores incorporate few risk factors and are not generalisable to the UK. We present a protocol for a UK study aiming to develop a detailed diagnostic score for iGBC to reliably stratify patients by their risk of iGBC. Method This UK multicentre, cross-sectional, trainee-led study includes adult patients undergoing cholecystectomy for benign indications. It incorporates a feasibility study and an adaptive design, and has been submitted for ethical review. A parallel qualitative study will explore attitudes towards the risk of iGBC and the acceptability of SHA among patients and healthcare professionals. The feasibility phase will include 500 patients over six months, collecting data on patient risk factors for iGBC, preoperative investigations, operative findings, and histology results. Exclusion criteria include cholecystectomy as a secondary procedure, polyps over 5mm, and conditions known to increase the risk of biliary tract cancers. Results Following the feasibility phase, the recruitment rate and data completeness will be evaluated. Outcomes will be assessed against anticipated values, and collaborators will be surveyed to identify challenges with data collection. Necessary adjustments to the electronic case record form or overall protocol will be made, and the recruitment target will be recalculated based on an updated sample size calculation. When the study meets specified progression criteria, additional sites will be recruited, and data collection will continue to meet the required sample size. A detailed statistical analysis plan will be provided at a later date. Conclusion This protocol describes a UK diagnostic modelling study aimed at developing a risk-stratification score for iGBC in patients undergoing routine cholecystectomy, identifying high-risk patients and facilitating SHA.

Multi-omic molecular characterization and diagnostic biomarkers for occult hepatitis B infection and HBsAg-positive hepatitis B infection

BackgroundThe pathological and physiological characteristics between HBsAg-positive HBV infection and occult hepatitis B infection (OBI) are currently unclear. This study aimed to explore the immune microenvironment in the peripheral circulation of OBI patients through integration of proteomic and metabolomic sequencing, and to identify molecular biomarkers for clinical diagnosis of HBsAg-positive HBV and OBI.MethodsThis research involved collection of plasma from 20 patients with OBI (negative for HBsAg but positive for HBV DNA, with HBV DNA levels < 200 IU/mL), 20 patients with HBsAg-positive HBV infection, and 10 healthy individuals. Mass spectrometry-based detection was used to analyze the proteome, while nuclear magnetic resonance spectroscopy was employed to study the metabolomic phenotypes. Differential molecule analysis, pathway enrichment and functional annotation, as well as weighted correlation network analysis (WGCNA), were conducted to uncover the characteristics of HBV-related liver disease. Prognostic biomarkers were identified using machine learning algorithms, and their validity was confirmed in a larger cohort using enzyme linked immunosorbent assay (ELISA).ResultsHBsAg-positive HBV individuals showed higher ALT levels (p=0.010) when compared to OBI patients. The influence of HBV infection on metabolic functions and inflammation was evident through the analysis of distinct metabolic pathways in HBsAg-positive HBV and OBI groups. Tissue tracing demonstrated a connection between Kupffer cells and HBsAg-positive HBV infection, as well as between hepatocytes and OBI. Immune profiling revealed the correlation between CD4 Tem cells, memory B cells and OBI, enabling a rapid response to infection reactivation through cytokine secretion and antibody production. A machine learning-constructed and significantly expressed molecule-based diagnostic model effectively differentiated HBsAg-positive and OBI groups (AUC values > 0.8). ELISA assay confirmed the elevation of FGB and FGG in OBI samples, suggesting their potential as biomarkers for distinguishing OBI from HBsAg-positive infection.ConclusionsThe immune microenvironment and metabolic status of HBsAg-positive HBV patients and OBI patients vary significantly. The machine learning-based diagnostic model described herein displayed impressive classification accuracy, presenting a non-invasive means of differentiating between OBI and HBsAg-positive HBV infections.

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review

Abstract Objectives This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models. Materials and Methods PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework. Results Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity. Discussion and Conclusion Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed

Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Abstract Modern radial compressors are designed to operate with high performance and a wide range of applications while minimizing their environmental impact. It is necessary to study the machine near the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two-stage radial compressor for refrigerant gas underwent extensive simulation, capturing unsteady RANS conditions and generating a substantial dataset of pressure signals from multiple probes. Selected signals, coupled with detailed computational fluid dynamics (CFD) post-processing, revealed fluid dynamic structures near surge conditions. This study utilizes all pressure signals to assess the stage's operational state (stable, transient, or unstable/surge). An additional goal is to develop an algorithm predicting flow patterns in different stage sections with minimal pressure signals at the rotor inlet. This is a preliminary step toward the development of a smart monitoring and diagnostic model for the compressor installed in a plant. The developed model consists of three submodels, trained with CFD results obtained from unsteady Reynolds averaged Navier–Stokes (URANS) simulations. The three submodels are a regression submodule, a classification submodule, and a forecasting algorithm. The regression submodule predicts diffuser static pressure fields, the classification submodule categorizes whether the compressor is in a critical condition or not, and the forecasting algorithm predicts the inducer pressure signals in the future impeller rotations according to the actual operation history. These sub-modules work together to forecast whether the compressor, according to the operation strategy, is going into instability conditions.

Neuroimage-Based Stroke Identification: A Machine Learning Approach

Stroke diagnosis is a time-critical process that requires rapid and accurate identification to ensure timely treatment. This study proposes a machine learning-based diagnostic model for stroke identification using neuro images. Early identification and timely intervention are critical to improving outcomes for stroke patients, but current diagnostic techniques, such as CT and MRI scans, often require time-consuming expert analysis. These delays can limit the effectiveness of treatment, particularly in acute cases where every minute counts. The problem lies in the need for faster, more reliable diagnostic tools that can analyze neuroimaging data with high accuracy and minimal human intervention. Machine learning, specifically deep learning, offers a promising solution to address this gap by automating the process of stroke detection. We employed a comprehensive approach, utilizing Inceptionv3, MobileNet, Convolutional Neural Network (CNN) algorithms to analyze neuroimages and predict stroke occurrence. This research proposes a machine learning-based diagnostic model for stroke identification using neuroimages, leveraging the power of Convolutional Neural Networks (CNN), with Inception V3 and MobileNet architectures. Inception V3, known for its ability to capture intricate image features through deep convolutional layers, and MobileNet, optimized for efficiency and speed, were employed to process large datasets of brain scans. The model was trained on these neuroimaging datasets to distinguish between healthy brain tissues and those affected by stroke. The combination of these two architectures allows for both detailed analysis and fast processing, making the model adaptable to clinical settings. The results showed that the model achieved a high accuracy rate in stroke identification, demonstrating its potential to assist healthcare professionals in diagnosing stroke faster and more accurately. By integrating this machine learning model into existing diagnostic workflows, it could significantly reduce the time to diagnosis, enabling earlier treatment and ultimately improving patient outcomes. Our model has the potential to enhance patient outcomes and reduce the economic burden of stroke. By leveraging the power of these advanced machine learning techniques, the model aims to enhance the efficiency and accuracy of stroke diagnosis compared to traditional methods

BIOM-10. CLINICAL PERFORMANCE EVALUATION OF A BRAIN CANCER LIQUID BIOPSY

Abstract Diagnostic delays impact the quality of life and survival of patients with brain cancer. Currently, with no blood test to assist them, clinicians must make their referral decision based on symptoms. Existing symptom-based referral guidelines inadequately stratify patients for brain imaging based on suspicion of cancer. A simple, rapid liquid biopsy placed in a primary care setting would enable more efficient triage of patients with non-specific symptoms related to brain cancer. The Dxcover® Liquid Biopsy is a rapid multi-omic liquid biopsy that interrogates a blood sample with infrared radiation and produces a distinctive signature that represents the whole biomolecular profile of the sample, and is sensitive to the hallmarks of brain cancer. The nature of the technology also allows for the diagnostic models to be tailored towards higher sensitivity (or specificity) depending on clinical priorities and international healthcare markets. In initial feasibility studies, we recruited 988 patients prospectively with non-specific symptoms associated with a brain tumor, and the algorithm detected 96% of the patients with brain tumors and 100% of glioblastomas (GBM), when tuned for greater sensitivity. EMBRACE is a prospective, observational, multicenter study, currently recruiting patients across seven sites in the United Kingdom, Belgium, Sweden and Switzerland. The study duration is 24 months and will recruit a minimum of 2200 participants. The target population is comprised of patients presenting to primary care with non-specific symptoms that are associated with brain cancer, such as headache and new onset neurological defect. The primary objective is to determine the clinical performance of the liquid biopsy for patients with brain cancer, in terms of diagnostic sensitivity and specificity. The test performance will be determined by comparing the liquid biopsy result to diagnostic imaging. Earlier and expeditious diagnoses in these patients are crucial to reduce the morbidities and mortalities associated with brain cancer. This simple, non-invasive liquid biopsy would facilitate the triage of brain tumor patients for rapid imaging.

Sensitivity of Bayesian Networks to Noise in Their Parameters

There is a widely spread belief in the Bayesian network (BN) community that the overall accuracy of results of BN inference is not too sensitive to the precision of their parameters. We present the results of several experiments in which we put this belief to a test in the context of medical diagnostic models. We study the deterioration of accuracy under random symmetric noise but also biased noise that represents overconfidence and underconfidence of human experts.Our results demonstrate consistently, across all models studied, that while noise leads to deterioration of accuracy, small amounts of noise have minimal effect on the diagnostic accuracy of BN models. Overconfidence, common among human experts, appears to be safer than symmetric noise and much safer than underconfidence in terms of the resulting accuracy. Noise in medical laboratory results and disease nodes as well as in nodes forming the Markov blanket of the disease nodes has the largest effect on accuracy. In light of these results, knowledge engineers should moderately worry about the overall quality of the numerical parameters of BNs and direct their effort where it is most needed, as indicated by sensitivity analysis.

Cross-sectional study on the diagnostic significance of plasma exosomal miRNAs in HBV-related hepatocellular carcinoma

PurposeHepatocellular carcinoma (HCC) associated with Hepatitis B Virus (HBV) is one of the most severe malignancies in East Asia, where early diagnosis is crucial for improving patient prognosis. So we aim to identify effective early diagnostic model for HCC.Design and methodsWe enrolled 108 early-stage HCC patients and 102 non-HCC individuals underlying HBV infection, collecting plasma exosomal miRNAs (exo-miRNAs) from all participants. These patients were randomly assigned to sequencing, screening, training, and validation group. After preliminary screening of candidate exo-miRNAs by next-generation high-throughput sequencing, qPCR data from the screening group were utilized in conjunction with the random forest machine learning algorithm to identify candidate exo-miRNAs with diagnostic potential. Subsequently, logistic regression diagnostic model was constructed using the relative expression levels of candidate exo-miRNAs, alpha-fetoprotein (AFP) levels and clinical parameters of gender and the presence of cirrhosis from the training group. The diagnostic accuracy of diagnostic model was subsequently validated in the validation group.ResultsFirstly, we identified miR-212-5p, miR-1248, and miR-1250-5p as candidate exo-miRNAs with potential diagnostic value. The exo-miRNAs panel, which consisted of miR-212-5p, miR-1248, miR-1250-5p, along with clinical parameters of gender and cirrhosis, achieved an AUC of 0.8634 (95% CI: 0.8027–0.9241), demonstrating diagnostic performance non-inferior to AFP in the independent dataset. Subsequently, by combining exo-miRNAs, AFP level and clinical parameter of gender, we enhanced the diagnostic panel, miRAGe, which exhibited an AUC of 0.9499 (95% CI: 0.9192–0.9806), sensitivity of 0.8900, and specificity of 0.9468.ConclusionOur study indicates that the miRAGe panel has low rate of both missed diagnosis and misdiagnosis rates, potentially serving as a useful diagnostic tool for HBV-related HCC in early stage, which may subsequently contribute to improve the prognosis.

Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.

There is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method. A total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye. The improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P < 0.001). The weighted Kappa test indicated medium consistency between the Indicatorv and the VASIchange (K = 0.567, P < 0.05). The optimal segmented model, HR-Net, had 0.8421 mIOU. The model-based severity evaluation results were not significantly different among the three experienced dermatologists. This study proposes an objective, accurate, and convenient hybrid model for diagnosing HD and evaluating the severity of vitiligo, providing a method for dermatologists especially in grassroots hospitals, and provides a foundation for telemedicine.

Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty estimation. Tools and packages that extend the reliability features of deep learning frameworks such as TensorFlow and PyTorch are also being explored and evaluated. Existing evaluation metrics for robustness are additionally being discussed and evaluated. This paper concludes by discussing limitations in the existing literature and possible future research directions to continue enhancing the status of this research topic, particularly in the medical domain, with the aim of ensuring that AI systems are trustworthy, reliable, and stable.

Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods

Cardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the severity of the condition. A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. With their extensive methodologies that would help in the early CVD prediction and recognition of behavioral patterns in large amounts of data, artificial intelligence, and data mining disciplines offer a broad study potential. The results of these predictions will help physicians make decisions and early diagnoses, decreasing the risk of patient death. This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. The review encompasses commonly employed methodologies such as support vector machine, decision tree, random forest, and convolutional neural networks (CNNs). Additionally, this survey aggregates and presents information on the performance metrics used to report accuracy. It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). In addition, it emphasizes prominent publishers, journals, and conferences that serve as platforms for the evaluation of scholarly works. Additionally, it will facilitate their understanding of the unresolved challenges or hurdles experienced by past researchers. A lack of more extensive and consistent datasets was the most common issue, followed by the need to improve existing models.

Diagnostic value of TRIM22 in diabetic kidney disease and its mechanism.

Diabetic kidney disease (DKD) is the primary reason of chronic kidney disease. Our objective was to discover potential autophagy-related biomarkers of tubulointerstitial injury in DKD and assess their clinical value. We retrieved four datasets (GSE104954, GSE30122, GSE30529, and GSE99340) of renal tubule samples from Gene Expression Omnibus (GEO) and used two algorithms (LASSO and SVM-RFE) to screen for autophagy-related differentially expressed genes (ARDEGs) in DKD. Tripartite motif containing 22 (TRIM22) was identified for subsequent validation. Validation of TRIM22 and autophagic indicators expression in clinical samples and HK-2 cells stimulated by high glucose using immunohistochemistry, immunofluorescence, and western blot. We identified four ARDEGs (TRIM22, PLK2, HTR2B, and FAS) using a diagnostic gene model. ROC curves further confirmed that TRIM22 had the best diagnostic efficacy for DKD. Both clinical samples and HK-2 cells stimulated by high glucose showed high protein expression of TRIM22. The correlation analysis revealed that TRIM22 correlates with SQSTM1, NGAL, and some clinical and pathological indicators in patients with DKD. We identified TRIM22 as a potential diagnostic biomarker for DKD, revealing its high diagnostic value in patients with DKD with moderate-to-severe interstitial fibrosis and tubular atrophy (IFTA). TRIM22 is involved in tubulointerstitial injury and autophagy dysregulation in DKD.

Heart Sound Classification Using Harmonic and Percussive Spectral Features from Phonocardiograms with a Deep ANN Approach

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with a particularly high burden in India. Non-invasive methods like Phonocardiogram (PCG) analysis capture the acoustic activity of the heart. This holds significant potential for the early detection and diagnosis of heart conditions. However, the complexity and variability of PCG signals pose considerable challenges for accurate classification. Traditional methods of PCG signal analysis, including time-domain, frequency-domain, and time-frequency domain techniques, often fall short in capturing the intricate details necessary for reliable diagnosis. This study introduces an innovative approach that leverages harmonic–percussive source separation (HPSS) to extract distinct harmonic and percussive spectral features from PCG signals. These features are then utilized to train a deep feed-forward artificial neural network (ANN), classifying heart conditions as normal or abnormal. The methodology involves advanced digital signal processing techniques applied to PCG recordings from the PhysioNet 2016 dataset. The feature set comprises 164 attributes, including the Chroma STFT, Chroma CENS, Mel-frequency cepstral coefficients (MFCCs), and statistical features. These are refined using the ROC-AUC feature selection method to ensure optimal performance. The deep feed-forward ANN model was rigorously trained and validated on a balanced dataset. Techniques such as noise reduction and outlier detection were used to improve model training. The proposed model achieved a validation accuracy of 93.40% with sensitivity and specificity rates of 82.40% and 80.60%, respectively. These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. This research highlights the potential for deploying such models in non-invasive cardiac diagnostics, particularly in resource-constrained settings. It also lays the groundwork for future advancements in cardiac signal analysis.

Pre and post diagnostic dementia care in four Scottish prisons

Background and purposeThe number of older people in prisons is increasing across the globe. Many have poor physical and mental health, higher prevalence of head injury, cognitive impairment and dementia than found in community populations. Meeting the complex needs of this vulnerable group has become an increasing concern for prison and prison healthcare services. The aim of this multi method qualitative study was to investigate how men with diagnosed or suspected dementia were identified, assessed, and cared for in Scottish prisons. It also explored the lived experience of individuals being assessed for or diagnosed with dementia within four prisons. The data from twenty nine interviews was thematically analysed and used to collaboratively propose principles for dementia care in prison and present the resultant co-designed care pathway.ResultsAt the time of data collection almost all the men known to have a dementia diagnosis or suspected dementia had complex health and social care needs, and some were living with advanced dementia. Prison healthcare staff reported taking a ‘case by case’ approach to their pre- and post-diagnostic care. Meeting these prisoner’s needs was complicated by the absence of organisational leads for care of older adults or people with dementia and there was no pathway or model in place to guide staff. Prison healthcare teams often had difficulty accessing specialist community services to support diagnosis. There was a lack of dementia education and knowledge about how to provide pre and post diagnostic dementia care in this setting amongst staff. The findings arising from this research have informed the co-production of two important evidence informed innovations namely a Model of Care and a pre- and post-diagnostic Care Pathway.ConclusionThis research adds insights critical to understanding the adequacy of current approaches to meeting dementia related needs within the prison setting. To our knowledge this paper offers the first co-produced evidence informed pre- and post- diagnostic dementia care pathway and model of care for use in prisons. These could serve as tools for change that could enable prison healthcare staff to deliver the right care, at the right time, by the right people, and provide an opportunity to assess risk and plan care for the future.

Analysis of economic crises at enterprises: from classical theories to innovative solutions

The article is devoted to a comprehensive analysis of economic crises at enterprises arising as a result of various internal and external factors that affect their stability and competitiveness. The author examines the polysemantic nature of the concept of «crisis», analyzing it in the context of various scientific disciplines and approaches. Special attention is paid to the dualistic approach to the crisis, where it is considered as a phenomenon that simultaneously carries threats and opportunities for development. The article provides a retrospective review of approaches to the study of crisis phenomena, starting from the cyclical approach, which interprets the crisis as a natural stage of the economic cycle, to modern system-synergistic methodologies, which consider the crisis as a prerequisite for self-organization and development of the enterprise. The conducted analysis of literary sources confirms the need to integrate various approaches for complex anti-crisis management at enterprises. The work also identifies unresolved aspects of the problem, including early diagnosis of crises, integration of anti-crisis tools, and the impact of digitalization on crisis processes. The article emphasizes the importance of flexible and adaptive diagnostic models capable of responding effectively to rapid changes in the business environment. The scientific novelty consists in proposing an action algorithm for diagnosing the crisis state of enterprises, based on the definition and analysis of key parameters that reflect the economic health of the business. The author emphasizes the need for a deep understanding of the specifics of each enterprise for successful anti-crisis management and the potential use of crisis phenomena to achieve new heights of development.The variety of interpretations of this term indicates its complexity and multifacetedness, which requires special attention to specify the meaning of the concept not only in the economic context, but also at the level of enterprises, underlines the relevance of this topic.

Compare three deep learning-based artificial intelligence models for classification of calcified lumbar disc herniation: a multicenter diagnostic study

ObjectiveTo develop and validate an artificial intelligence diagnostic model for identifying calcified lumbar disc herniation based on lateral lumbar magnetic resonance imaging(MRI).MethodsDuring the period from January 2019 to March 2024, patients meeting the inclusion criteria were collected. All patients had undergone both lumbar spine MRI and computed tomography(CT) examinations, with regions of interest (ROI) clearly marked on the lumbar sagittal MRI images. The participants were then divided into separate sets for training, testing, and external validation. Ultimately, we developed a deep learning model using the ResNet-34 algorithm model and evaluated its diagnostic efficacy.ResultsA total of 1,224 eligible patients were included in this study, consisting of 610 males and 614 females, with an average age of 53.34 ± 10.61 years. Notably, the test datasets displayed an impressive classification accuracy rate of 91.67%, whereas the external validation datasets achieved a classification accuracy rate of 88.76%. Among the test datasets, the ResNet34 model outperformed other models, yielding the highest area under the curve (AUC) of 0.96 (95% CI: 0.93, 0.99). Additionally, the ResNet34 model also exhibited superior performance in the external validation datasets, exhibiting an AUC of 0.88 (95% CI: 0.80, 0.93).ConclusionIn this study, we established a deep learning model with excellent performance in identifying calcified intervertebral discs, thereby offering a valuable and efficient diagnostic tool for clinical surgeons.

A Lightweight and Efficient Multimodal Feature Fusion Network for Bearing Fault Diagnosis in Industrial Applications

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.