Int J Biol Macromol. 2024 Nov 25:138089. doi: 10.1016/j.ijbiomac.2024.138089. Online ahead of print.

ABSTRACT

Edible fungal polysaccharides have emerged as significant bioactive compounds with diverse therapeutic potentials, including notable anti-tumor effects. Derived from various fungal sources, these polysaccharides exhibit complex biological activities such as antioxidant, immune-modulatory, anti-inflammatory, and anti-obesity properties. In cancer therapy, members of this family show promise in inhibiting tumor growth and metastasis through mechanisms like apoptosis induction and modulation of the immune system. This review provides a detailed examination of contemporary techniques for the targeted isolation and structural elucidation of edible fungal polysaccharides. Additionally, the review highlights the application of advanced artificial intelligence (AI) methodologies to facilitate efficient and accurate structural analysis of these polysaccharides. It also explores their interactions with immune cells within the tumor microenvironment and their role in modulating gut microbiota, which can enhance overall immune function and potentially reduce cancer risks. Clinical studies further demonstrate their efficacy in various cancer treatments. Overall, edible fungal polysaccharides represent a promising frontier in cancer therapy, leveraging their natural origins and minimal toxicity to offer novel strategies for comprehensive cancer management.

PMID:39603293 | DOI:10.1016/j.ijbiomac.2024.138089

Comput Biol Med. 2024 Nov 26;184:109354. doi: 10.1016/j.compbiomed.2024.109354. Online ahead of print.

ABSTRACT

Medical images often require rescaling to various spatial resolutions to ensure interpretations at different levels. Conventional deep learning-based image super-resolution (SR) enhances the fixed-scale resolution. Implicit neural representation (INR) is a promising way of achieving arbitrary-scale image SR. However, existing INR-based methods require the repeated execution of the neural network (NN), which is slow and inefficient. In this paper, we present Neural Explicit Representation (NExpR) for fast arbitrary-scale medical image SR. Our algorithm represents an image with an explicit analytical function, whose input is the low-resolution image and output is the parameterization of the analytical function. After obtaining the analytical representation through a single NN inference, SR images of arbitrary scales can be derived by evaluating the explicit functions at desired coordinates. Because of the analytical explicit representation, NExpR is significantly faster than INR-based methods. In addition to speed, our method achieves on-par or better image quality than other strong competitors. Extensive experiments on Magnetic Resonance Imaging (MRI) datasets, including ProstateX, fastMRI, and our in-house clinical prostate dataset, as well as the Computerized Tomography (CT) dataset, specifically the Medical Segmentation Decathlon (MSD) liver dataset, demonstrate the superiority of our method. Our method reduces the rescaling time from the order of 1 ms to the order of 0.01 ms, achieving an over 100× speedup without losing the image quality. Code is available at https://github.com/Calvin-Pang/NExpR.

PMID:39602975 | DOI:10.1016/j.compbiomed.2024.109354

Biomed Tech (Berl). 2024 Nov 28. doi: 10.1515/bmt-2023-0080. Online ahead of print.

ABSTRACT

OBJECTIVES: No standard, objective diagnostic procedure exists for most neurological diseases causing tremors. Therefore, drawing tests have been widely analyzed to support diagnostic procedures. In this study, we examine the comparison of Archimedean spiral and line drawings, the possibilities of their joint application, and the relevance of displaying pressure on the drawings to recognize Parkinsonism and cerebellar dysfunction. We further attempted to use an automatic processing and evaluation system.

METHODS: Digital images were developed from raw data by adding or omitting pressure data. Pre-trained (MobileNet, Xception, ResNet50) models and a Baseline (from scratch) model were applied for binary classification with a fold cross-validation procedure. Predictions were analyzed separately by drawing tasks and in combination.

RESULTS: The neurological diseases presented here can be recognized with a significantly higher macro f1 score from the spiral drawing task (up to 95.7 %) than lines (up to 84.3 %). A significant improvement can be achieved if the spiral is supplemented with line drawing. The pressure inclusion in the images did not result in significant information gain.

CONCLUSIONS: The spiral drawing has a robust recognition power and can be supplemented with a line drawing task to increase the correct recognition. Moreover, X and Y coordinates appeared sufficient without pressure with this methodology.

PMID:39602901 | DOI:10.1515/bmt-2023-0080

Biomed Phys Eng Express. 2024 Nov 27. doi: 10.1088/2057-1976/ad97c2. Online ahead of print.

ABSTRACT

Objective
Attenuation correction of PET data is commonly conducted through the utilization of a secondary imaging technique to produce attenuation maps. The customary approach to attenuation correction, which entails the employment of CT images, necessitates energy conversion. However, the present study introduces a novel deep learning-based method that obviates the requirement for CT images and energy conversion.
Methods
This study employs a residual Pix2Pix network to generate attenuation-corrected PET images using the 4033 2D PET images of 37 healthy adult brains for train and test. The model, implemented in TensorFlow and Keras, was evaluated by comparing image similarity, intensity correlation, and distribution against CT-AC images using metrics such as PSNR and SSIM for image similarity, while a 2D histogram plotted pixel intensities. Differences in standardized uptake values (SUV) demonstrated the model's efficiency compared to the CTAC method.
Results
The residual Pix2Pix demonstrated strong agreement with the CT-based attenuation correction, the proposed network yielding MAE, MSE, PSNR, and MS-SSIM values of 3×10-3, 2×10-4, 38.859, and 0.99, respectively. The residual Pix2Pix model's results showed a negligible mean SUV difference of 8×10-4(P-value = 0.10), indicating its accuracy in PET image correction. The residual Pix2Pix model exhibits high precision with a strong correlation coefficient of R2 = 0.99 to CT-based methods. The findings indicate that this approach surpasses the conventional method in terms of precision and efficacy.
Conclusions
The proposed residual Pix2Pix framework enables accurate and feasible attenuation correction of brain F-FDG PET without CT. However, clinical trials are required to evaluate its clinical performance. The PET images reconstructed by the framework have low errors compared to the accepted test reliability of PET/CT, indicating high quantitative similarity.&#xD.

PMID:39602833 | DOI:10.1088/2057-1976/ad97c2

Biomed Phys Eng Express. 2024 Nov 27. doi: 10.1088/2057-1976/ad97c1. Online ahead of print.

ABSTRACT

Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible. This work aims to improve the efficiency of 4D-CBCT MoCo reconstruction using DL-based registration for the rapid generation of a motion model prior to treatment. 
Approach. An artifact-reduction DL model was first used to improve the initial 4D-CBCT reconstruction by reducing streaking artifacts. Based on the artifact-reduced phase images, a groupwise DIR employing DL was used to estimate the inter-phase motion model. Two DL DIR models using different learning strategies were employed: 1) a patient-specific one-shot DIR model which was trained from scratch only using the images to be registered, and 2) a population DIR model which was pre-trained using collected 4D-CT images from 35 patients. The registration accuracy of two DL DIR models was assessed and compared to a conventional groupwise DIR approach implemented in the Elastix toolbox using the publicly available DIR-Lab dataset, a Monte Carlo simulation dataset from the SPARE challenge, and two clinical cases. 
Main results. The patient-specific DIR model and the population DIR model demonstrated registration accuracy comparable to the conventional state-of-the-art methods on the DIR-Lab dataset. No significant difference in image quality was observed between the final MoCo reconstructions using the patient-specific model and population model for motion modeling, compared to using the conventional approach. The average runtime (hh:mm:ss) of the entire MoCo reconstruction on SPARE dataset was reduced from 01:37:26 using conventional DIR method to 00:10:59 using patient-specific model and 00:01:05 using the pre-trained population model. 
Significance. DL-based registration methods can improve the efficiency in generating motion models for 4D-CBCT without compromising the performance of final MoCo reconstruction.

PMID:39602831 | DOI:10.1088/2057-1976/ad97c1

J Crohns Colitis. 2024 Nov 27:jjae180. doi: 10.1093/ecco-jcc/jjae180. Online ahead of print.

ABSTRACT

INTRODUCTION: Ulcerative colitis (UC) management employs a strategy targeting histological and endoscopic remission. Correlation of white-light endoscopy (WLE) scores with histological activity is limited. Single-wavelength endoscopy (SWE) addressing microvascular changes reflecting histological disease activity, may better assess histological remission.

AIMS AND METHODS: Our goal was to assess the accuracy of a computer-aided diagnosis (CAD) system for histological activity estimation in UC, based on either WLE or SWE. We collected 6926 sets of corresponding WLE and SWE frames in 112 patients with UC, using a prototype endoscopic system enabling both imaging methods (FUJIFILM, Tokyo, Japan). Histological remission (Geboes score ≤2B.0) assessed at the location of imaging was annotated for all frames and separate WLE-CAD and SWE-CAD models were trained using deep learning for automated detection of histological remission with either imaging modality.

RESULTS: Initial training of both models on the same subset of 42 patients, resulted in SWE-CAD outperforming WLE-CAD with a mean sensitivity of 88.0% vs 73.9% (p < 0.001), a mean specificity of 71.7% vs 65.6% (p=0.45), and a diagnostic accuracy of 83.3% vs 67.5% (p<0.005), respectively. Further training of the SWE-CAD model on the entire dataset of 112 patients resulted in SWE-CAD achieving a 95.2% accuracy, 96.4% sensitivity, and 92.9% specificity on a section level.

CONCLUSION: By utilizing automated CAD based on non-magnifying SWE for enhanced capillary visibility versus WLE, histological remission was detected with 95.2% diagnostic accuracy in patients with UC, offering stable objectivity and helping to exclude inter-reader variability.

PMID:39602814 | DOI:10.1093/ecco-jcc/jjae180

Opt Lett. 2024 Dec 1;49(23):6773-6776. doi: 10.1364/OL.545435.

ABSTRACT

Vortex beams (VBs) have the potential to support high-capacity optical communications. However, a typical VB carries only a single orbital angular momentum (OAM) in space, limiting its high-capacity communication. We propose controllably simultaneous generation of high-quality VB arrays with multiple OAMs, creating the independent multi-channel space in which the OAM mode can be flexibly manipulated at the corresponding spatial location. We then demonstrate a VB array-based multi-channel optical communication system combining a custom-designed Dammam grating and an integrated vortex grating, with the help of a designed single-input multiple-output deep learning recognition model. Experimental demonstration of the simultaneous transmission of four grayscale images was verified, with an average error rate of less than 0.003 without turbulence and 0.061 with turbulence. The proposed multi-channel method (multi-image transmission) can significantly increase the versatility of the VB array and further broaden its application in high-capacity optical communications.

PMID:39602747 | DOI:10.1364/OL.545435

Hum Reprod. 2024 Nov 27:deae263. doi: 10.1093/humrep/deae263. Online ahead of print.

ABSTRACT

Grading of blastocyst morphology is used routinely for embryo selection with good outcomes. A lot of effort has been placed in IVF to search for the prize of selecting the most viable embryo to transfer ('the golden fleece of embryology'). To improve on morphology alone, artificial intelligence (AI) has also become a tool of interest, with many retrospective studies being published with impressive prediction capabilities. Subsequently, AI has again raised expectations that this 'golden fleece of embryology' was once again within reach. A recent RCT however was not able to demonstrate non-inferiority using a deep learning algorithm 'iDAScore version 1' for clinical pregnancy rate when compared to standard morphology. Good blastocyst morphology has again proven itself as a high bar in predicting live birth. We should however not give up on the development of further approaches which may allow us to identify extra features of viable embryos that are not captured by morphology.

PMID:39602554 | DOI:10.1093/humrep/deae263

Sci Adv. 2024 Nov 29;10(48):eadr2641. doi: 10.1126/sciadv.adr2641. Epub 2024 Nov 27.

ABSTRACT

Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering.

PMID:39602544 | DOI:10.1126/sciadv.adr2641

Pharmacol Rep. 2024 Nov 27. doi: 10.1007/s43440-024-00679-1. Online ahead of print.

ABSTRACT

BACKGROUND: Serotonergic psychedelics, which display a high affinity and specificity for 5-HT2A receptors like 2,5-dimethoxy-4-iodoamphetamine (DOI), reliably induce a head-twitch response in rodents characterized by paroxysmal, high-frequency head rotations. Traditionally, this behavior is manually counted by a trained observer. Although automation could simplify and facilitate data collection, current techniques require the surgical implantation of magnetic markers into the rodent's skull or ear.

METHODS: This study aimed to assess the feasibility of a marker-less workflow for detecting head-twitch responses using deep learning algorithms. High-speed videos were analyzed using the DeepLabCut neural network to track head movements, and the Simple Behavioral Analysis (SimBA) toolkit was employed to build models identifying specific head-twitch responses.

RESULTS: In studying DOI (0.3125-2.5 mg/kg) effects, the deep learning algorithm workflow demonstrated a significant correlation with human observations. As expected, the preferential 5-HT2A receptor antagonist ketanserin (0.625 mg/kg) attenuated DOI (1.25 mg/kg)-induced head-twitch responses. In contrast, the 5-HT5A receptor antagonists SB 699,551 (3 and 10 mg/kg), and ASP 5736 (0.01 and 0.03 mg/kg) failed to do so.

CONCLUSIONS: Previous drug discrimination studies demonstrated that the 5-HT5A receptor antagonists attenuated the interoceptive cue of a potent hallucinogen LSD, suggesting their anti-hallucinatory effects. Nonetheless, the present results were not surprising and support the head-twitch response as selective for 5-HT2A and not 5-HT5A receptor activation. We conclude that the DeepLabCut and SimBA toolkits offer a high level of objectivity and can accurately and efficiently identify compounds that induce or inhibit head-twitch responses, making them valuable tools for high-throughput research.

PMID:39602080 | DOI:10.1007/s43440-024-00679-1

Environ Monit Assess. 2024 Nov 27;196(12):1264. doi: 10.1007/s10661-024-13447-8.

ABSTRACT

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists. In addressing this challenge, deep learning models have garnered substantial attention in research due to their outstanding performance across diverse applications. However, the optimization of hyperparameters and features remains a challenging task. This study seeks to leverage historical data to construct the long short-term memory-based model for forecasting multistep PM10. To refine its architecture, a modified genetic algorithm is employed for automatic design. Furthermore, we explore principal component analysis and exhaustive feature selection to identify the optimal feature set. This paper introduces a novel hybrid deep learning model named EFS-GA-LSTM, tailored for multistep hourly PM10 forecasting. To assess its performance, we compare it with other hyperparameter optimization algorithms, including particle swarm optimization, variable neighborhood search, and Bayesian optimization with Gaussian process. The input dataset comprises hourly PM10 concentrations, meteorological variables, and time variables. The results reveal that for 3-h-ahead forecasting tasks, the EFS-GA-LSTM network demonstrates improvements in root mean square error, mean absolute percentage error, correlation coefficient, and coefficient of determination.

PMID:39601991 | DOI:10.1007/s10661-024-13447-8

Mol Pharm. 2024 Nov 27. doi: 10.1021/acs.molpharmaceut.4c00863. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has spotlighted the potential of in vitro transcribed (IVT) mRNA vaccines with their demonstrated efficacy, safety, cost-effectiveness, and rapid manufacturing. Numerous IVT mRNA vaccines are now under clinical trials for a range of targets, including infectious diseases, cancers, and genetic disorders. Despite their promise, IVT mRNA vaccines face hurdles such as limited expression levels, nonspecific targeting beyond the liver, rapid degradation, and unintended immune activation. Overcoming these challenges is crucial to harnessing the full therapeutic potential of IVT mRNA vaccines for global health advancement. This review provides a comprehensive overview of the latest research progress and optimization strategies for IVT mRNA molecules and delivery systems, including the application of artificial intelligence (AI) models and deep learning techniques for IVT mRNA structure optimization and mRNA delivery formulation design. We also discuss recent development of the delivery platforms, such as lipid nanoparticles (LNPs), polymers, and exosomes, which aim to address challenges related to IVT mRNA protection, cellular uptake, and targeted delivery. Lastly, we offer insights into future directions for improving IVT mRNA vaccines, with the hope to spur further progress in IVT mRNA vaccine research and development.

PMID:39601789 | DOI:10.1021/acs.molpharmaceut.4c00863

Network. 2024 Nov 27:1-37. doi: 10.1080/0954898X.2024.2429721. Online ahead of print.

ABSTRACT

Thermal Power Plant is a common power plant that generates power by fuel-burning to produce electricity. Being a significant component of the energy sector, the Thermal Power Plant faces several issues that lead to reduced productivity. Conventional researchers have tried using different mechanisms for improvising the production of Thermal Power Plants in varied dimensions. Due to the diverse dimensions considered by existing works, the present review endeavours to afford a comprehensive summary of these works. To achieve this, the study reviews articles in the range (2019-2023) that are allied with the utility of SC methodologies (encompassing AI-ML (Machine Learning) and DL (Deep Learning) in enhancing the productivity of Thermal Power Plants by various dimensions. The conventional AI-based approaches are comparatively evaluated for effective contribution in improvising Thermal Power Plant production. Following this, a critical assessment encompasses the year-wise distribution and varied dimensions focussed by traditional studies in this area. This would support future researchers in determining the dimensions that have attained limited and high focus based on which appropriate research works can be performed. Finally, future suggestions and research gaps are included to offer new stimulus for further investigation of AI in Thermal Power Plants.

PMID:39601783 | DOI:10.1080/0954898X.2024.2429721

J Biomol Struct Dyn. 2024 Nov 27:1-13. doi: 10.1080/07391102.2024.2431190. Online ahead of print.

ABSTRACT

Lung adenocarcinoma is highly heterogeneous at the molecular level between different stages; therefore, understanding molecular mechanisms contributing to such heterogeneity is needed. In addition, multiple stages of progression are critical factors for lung adenocarcinoma treatment. However, previous studies showed that cancer progression is associated with altered lncRNA expression, highlighting the tissue-specific and developmental stage-specific nature of lncRNAs in various diseases. Therefore, a study using an integrated network approach to explore the role of lncRNA in carcinogenesis was done using expression profiles revealing stage-specific and conserved lncRNA biomarkers in lung adenocarcinoma. We constructed ceRNA networks for each stage of lung adenocarcinoma and analysed them using network topology, differential co-expression network, protein-protein interaction network, functional enrichment, survival analysis, genomic analysis and deep learning to identify potential lncRNA biomarkers. The co-expression networks of healthy and three successive stages of lung adenocarcinoma have shown different network properties. One conserved and four stage-specific lncRNAs are identified as genome regulatory biomarkers. These lncRNAs can successfully identify lung adenocarcinoma and different stages of progression using deep learning. In addition, we identified five mRNAs, four miRNAs and twelve novel carcinogenic interactions associated with the progression of lung adenocarcinoma. These lncRNA biomarkers will provide a novel perspective into the underlying mechanism of adenocarcinoma progression and may be further helpful in early diagnosis, treatment and prognosis of this deadly disease.

PMID:39601689 | DOI:10.1080/07391102.2024.2431190

J Biomol Struct Dyn. 2024 Nov 27:1-16. doi: 10.1080/07391102.2024.2430454. Online ahead of print.

ABSTRACT

Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as the second highest cause of death for women worldwide. Early BC prediction helps decrease the BC mortality rate and improve treatment plans. Ultrasound is a popular and widely used imaging technique to detect BC at an earlier stage. Segmenting and classifying the tumors from ultrasound images is difficult. This paper proposes an optimal deep learning (DL)-based BC detection system with effective pre-trained transfer learning models-based segmentation and feature learning mechanisms. The proposed system comprises five phases: preprocessing, segmentation, feature learning, selection, and classification. Initially, the ultrasound images are collected from the breast ultrasound images (BUSI) dataset, and the preprocessing operations, such as noise removal using the Wiener filter and contrast enhancement using histogram equalization, are performed on the collected data to improve the dataset quality. Then, the segmentation of cancer-affected regions from the preprocessed data is done using a dilated convolution-based U-shaped network (DCUNet). The features are extracted or learned from the segmented images using spatial and channel attention including densely connected convolutional network-121 (SCADN-121). Afterwards, the system applies an enhanced cuckoo search optimization (ECSO) algorithm to select the features from the extracted feature set optimally. Finally, the ECSO-tuned long short-term memory (ECSO-LSTM) was utilized to classify BC into '3' classes, such as normal, benign, and malignant. The experimental outcomes proved that the proposed system attains 99.86% accuracy for BC classification, which is superior to the existing state-of-the-art methods.

PMID:39601679 | DOI:10.1080/07391102.2024.2430454

Radiol Artif Intell. 2024 Nov;6(6):e240604. doi: 10.1148/ryai.240604.

NO ABSTRACT

PMID:39601670 | DOI:10.1148/ryai.240604

Radiol Artif Intell. 2024 Nov 27:e240032. doi: 10.1148/ryai.240032. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To apply conformal prediction to a deep learning (DL) model for intracranial hemorrhage (ICH) detection and evaluate model performance in detection as well as model accuracy in identifying challenging cases. Materials and Methods This was a retrospective (November 2017 through December 2017) study of 491 noncontrast head CT volumes from the CQ500 dataset in which three senior radiologists annotated sections containing ICH. The dataset was split into definite and challenging (uncertain) subsets, where challenging images were defined as those in which there was disagreement among readers. A DL model was trained on 146 patients (mean age = 45.7, 70 females, 76 males) from the definite data (training dataset) to perform ICH localization and classification into five classes. To develop an uncertainty-aware DL model, 1,546 sections of the definite data (calibration dataset) was used for Mondrian conformal prediction (MCP). The uncertainty-aware DL model was tested on 8,401 definite and challenging sections to assess its ability to identify challenging sections. The difference in predictive performance (P value) and ability to identify challenging sections (accuracy) were reported. Results After the MCP procedure, the model achieved an F1 score of 0.920 for ICH classification on the test dataset. Additionally, it correctly identified 6,837 of the 6,856 total challenging sections as challenging (99.7% accuracy). It did not incorrectly label any definite sections as challenging. Conclusion The uncertainty-aware MCP-augmented DL model achieved high performance in ICH detection and high accuracy in identifying challenging sections, suggesting its usefulness in automated ICH detection and potential to increase trustworthiness of DL models in radiology. ©RSNA, 2024.

PMID:39601654 | DOI:10.1148/ryai.240032

Neuroradiol J. 2024 Nov 27:19714009241303078. doi: 10.1177/19714009241303078. Online ahead of print.

ABSTRACT

PURPOSE: We aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH) cases on a real-world heterogeneous clinical dataset. Furthermore, we delved deeper into evaluating how patients' risk factors influenced the model's performance and gathered feedback on satisfaction from radiologists of varying ranks.

METHODS: This prospective IRB approved study included 5600 non-contrast CT scans of the head in various clinical settings, that is, emergency, inpatient, and outpatient units. The patients' risk factors were collected and tested for impacting the performance of DL model utilizing univariate and multivariate regression analyses. The performance of DL model was contrasted to the radiologists' interpretation to determine the presence or absence of ICH with subsequent classification into subcategories of ICH. Key metrics, including accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, were calculated. Receiver operating characteristics curve, along with the area under the curve, were determined. Additionally, a questionnaire was conducted with radiologists of varying ranks to assess their experience with the model.

RESULTS: The model exhibited outstanding performance, achieving a high sensitivity of 89% and specificity of 96%. Additional performance metrics, including positive predictive value (82%), negative predictive value (97%), and overall accuracy (94%), underscore its robust capabilities. The area under the ROC curve further demonstrated the model's efficacy, reaching 0.954. Multivariate logistic regression revealed statistical significance for age, sex, history of trauma, operative intervention, HTN, and smoking.

CONCLUSION: Our study highlights the satisfactory performance of the DL model on a diverse real-world dataset, garnering positive feedback from radiology trainees.

PMID:39601611 | DOI:10.1177/19714009241303078

Cardiol Rev. 2024 Nov 27. doi: 10.1097/CRD.0000000000000816. Online ahead of print.

ABSTRACT

Dyslipidemia is abnormal lipid and lipoprotein levels in the blood, influenced mainly by genetics, lifestyle, and environmental factors. The management of lipid levels in children involves early screening, nonpharmacological interventions such as lifestyle modifications and dietary changes, nutraceuticals, and pharmacological treatments, including drug therapy. However, the prevalence of dyslipidemia in the pediatric population is increasing, particularly among obese children, which is a significant risk factor for cardiovascular complications. This narrative review analyzes current literature on the management of dyslipidemia in children and explores the potential of artificial intelligence (AI) to improve screening, diagnosis, and treatment outcomes. A comprehensive literature search was conducted using Google Scholar and PubMed databases, focusing primarily on the application of AI in managing dyslipidemia. AI has been beneficial in managing lipid disorders, including lipid profile analysis, obesity assessments, and familial hypercholesterolemia screening. Deep learning models, machine learning algorithms, and artificial neural networks have improved diagnostic accuracy and treatment efficacy. While most studies are done in the adult population, the promising results suggest further exploring AI management of dyslipidemia in children.

PMID:39601582 | DOI:10.1097/CRD.0000000000000816

Quintessence Int. 2024 Nov 27;0(0):0. doi: 10.3290/j.qi.b5857664. Online ahead of print.

ABSTRACT

Dental caries is one of the most common diseases globally and affects children and adults living in poverty who have limited access to dental care the most. Left unexamined and untreated in the early stages, treatments for late-stage and severe caries are costly and unaffordable for socioeconomically disadvantaged families. If detected early, caries can be reversed to avoid more severe outcomes and a tremendous financial burden on the dental care system. Building upon a dataset of 50,179 intraoral tooth photos taken by various modalities, including smartphones and intraoral cameras, this study developed a multi-stage deep learning-based pipeline of AI algorithms that localize individual teeth and classify each tooth into several classes of caries. This study initially assigned International Caries Detection and Assessment System (ICDAS) scores to each tooth and subsequently grouped caries into two levels: Level-1 for white spots (ICDAS 1 and 2) and level-2 for cavitated lesions (ICDAS 3-6). The system's performance was assessed across a broad spectrum of anterior andposterior teeth photographs. For anterior teeth, 89.78% sensitivity and 91.67% specificity for level-1 (white spots) and 97.06% sensitivity and 99.79% specificity for level-2 (cavitated lesions) were achieved, respectively. For the more challenging posterior teeth due to the higher variability in the location of white spots, 90.25% sensitivity and 86.96% specificity for level-1 and 95.8% sensitivity and 94.12% specificity for level-2 were achieved, respectively. The performance of the developed AI algorithms shows potential as a cost-effective tool for early caries detection in non-clinical settings.

PMID:39601186 | DOI:10.3290/j.qi.b5857664