JMIR Form Res. 2025 Jan 29;9:e66330. doi: 10.2196/66330.

ABSTRACT

BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from hospitals and self-reported physician diagnoses or typical schizophrenia symptoms. These approaches are likely to underestimate the true prevalence owing to stigma, poor insight, or lack of access to health care among respondents. To address these issues, we previously developed an artificial neural network (ANN)-based schizophrenia classification model (SZ classifier) using data from a large-scale Japanese web-based survey to enhance the comprehensiveness of schizophrenia case identification in the general population. In addition, we also plan to introduce a population-based survey to collect general information and sample participants matching the population's demographic structure, thereby achieving a precise estimate of the prevalence of schizophrenia in Japan.

OBJECTIVE: This study aimed to estimate the prevalence of schizophrenia by applying the SZ classifier to random samples from the Japanese population.

METHODS: We randomly selected a sample of 750 participants where the age, sex, and regional distributions were similar to Japan's demographic structure from a large-scale Japanese web-based survey. Demographic data, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities were collected and applied to the SZ classifier, as this information was also used for developing the SZ classifier. The crude prevalence of schizophrenia was calculated through the proportion of positive cases detected by the SZ classifier. The crude estimate was further refined by excluding false-positive cases and including false-negative cases to determine the actual prevalence of schizophrenia.

RESULTS: Out of 750 participants, 62 were classified as schizophrenia cases by the SZ classifier, resulting in a crude prevalence of schizophrenia in the general population of Japan of 8.3% (95% CI 6.6%-10.1%). Among these 62 cases, 53 were presumed to be false positives, and 3 were presumed to be false negatives. After adjustment, the actual prevalence of schizophrenia in the general population was estimated to be 1.6% (95% CI 0.7%-2.5%).

CONCLUSIONS: This estimated prevalence was slightly higher than that reported in previous studies, possibly due to a more comprehensive disease classification methodology or, conversely, model limitations. This study demonstrates the capability of an ANN-based model to improve the estimation of schizophrenia prevalence in the general population, offering a novel approach to public health analysis.

PMID:39879582 | DOI:10.2196/66330

Brief Bioinform. 2024 Nov 22;26(1):bbaf039. doi: 10.1093/bib/bbaf039.

ABSTRACT

Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks. We validated our method effectiveness through a comparative study using a simulated dataset and RNA-Seq data from The Cancer Genome Atlas breast cancer dataset. Our method was benchmarked against traditional techniques (ORA, FCS), shallow machine learning models (logistic regression, support vector machine), and deep learning approaches (DeepHisCom, PASNet). The results demonstrate that GPNet outperforms these methods in low-SNR, large-sample datasets, where it remains robust and reliable, significantly reducing both Type I error and improving power. This makes our method well suited for pathway analysis in large, multi-center studies. The code can be found at https://github.com/haolu123/GPNet_pathway">https://github.com/haolu123/GPNet_pathway.

PMID:39879387 | DOI:10.1093/bib/bbaf039

Brief Bioinform. 2024 Nov 22;26(1):bbaf037. doi: 10.1093/bib/bbaf037.

ABSTRACT

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM. Colocalization analysis was performed to validate genetic associations, while scPagwas analysis was used to evaluate the relevance of key UDIPs to GBM at the cellular level. Among 512 UDIPs tested, 23 were found to have significant causal associations with GBM. Notably, UDIPs such as T1-33 (OR = 1.007, 95% CI = 1.001 to 1.012, P = .022), T1-34 (OR = 1.012, 95% CI = 1.001-1.023, P = .028), and T1-96 (OR = 1.009, 95% CI = 1.001-1.019, P = .046) were found to have a genetic association with GBM. Furthermore, T1-34 and T1-96 were significantly associated with GBM recurrence, with P-values < .0001 and P < .001, respectively. In addition, scPagwas analysis revealed that T1-33, T1-34, and T1-96 are distinctively linked to different GBM subtypes, with T1-33 showing strong associations with the neural progenitor-like subtype (NPC2), T1-34 with mesenchymal (MES2) and neural progenitor (NPC1) cells, and T1-96 with the NPC2 subtype. T1-33, T1-34, and T1-96 hold significant potential for predicting tumor recurrence and aiding in the development of personalized GBM treatment strategies.

PMID:39879386 | DOI:10.1093/bib/bbaf037

PLoS One. 2025 Jan 29;20(1):e0316493. doi: 10.1371/journal.pone.0316493. eCollection 2025.

ABSTRACT

The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. The proposed method constitutes a significant advancement in the field, providing a robust solution to the intricacies of microbiome data analysis and amplifying the potential of DL methods in disease detection.

PMID:39879257 | DOI:10.1371/journal.pone.0316493

Eur J Nucl Med Mol Imaging. 2025 Jan 29. doi: 10.1007/s00259-025-07091-8. Online ahead of print.

ABSTRACT

PURPOSE: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.

METHODS: We trained a generative model on 99mTc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis. A blinded reader study was performed to assess the clinical validity and quality of the generated data. We investigated the added value of the generated data by augmenting an independent small single-center dataset with synthetic data and by training a deep learning model to detect abnormal uptake in a downstream classification task. We tested this model on 7,472 scans from 6,448 patients across four external sites in a cross-tracer and cross-scanner setting and associated the resulting model predictions with clinical outcomes.

RESULTS: The clinical value and high quality of the synthetic imaging data were confirmed by four readers, who were unable to distinguish synthetic scans from real scans (average accuracy: 0.48% [95% CI 0.46-0.51]), disagreeing in 239 (60%) of 400 cases (Fleiss' kappa: 0.18). Adding synthetic data to the training set improved model performance by a mean (± SD) of 33(± 10)% AUC (p < 0.0001) for detecting abnormal uptake indicative of bone metastases and by 5(± 4)% AUC (p < 0.0001) for detecting uptake indicative of cardiac amyloidosis across both internal and external testing cohorts, compared to models without synthetic training data. Patients with predicted abnormal uptake had adverse clinical outcomes (log-rank: p < 0.0001).

CONCLUSIONS: Generative AI enables the targeted generation of bone scintigraphy images representing different clinical conditions. Our findings point to the potential of synthetic data to overcome challenges in data sharing and in developing reliable and prognostic deep learning models in data-limited environments.

PMID:39878897 | DOI:10.1007/s00259-025-07091-8

Br J Radiol. 2025 Jan 29:tqaf018. doi: 10.1093/bjr/tqaf018. Online ahead of print.

ABSTRACT

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalised medicine. This paper explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalised medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation and generative models. We explore their roles in tumour board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalisability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

PMID:39878877 | DOI:10.1093/bjr/tqaf018

Clin Oral Investig. 2025 Jan 29;29(2):97. doi: 10.1007/s00784-025-06183-x.

ABSTRACT

OBJECTIVES: To evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in dentistry.

METHODS: A comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and IEEE Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective studies.

RESULTS: Of the 493 articles identified, 22 met the inclusion criteria. Among these, 14 studies used geometry-based methods, 7 used artificial intelligence (AI) techniques, and 1 compared the accuracy of both approaches. Geometry-based methods primarily utilize two-stage coarse-to-fine registration algorithms, which require relatively fewer computational resources. In contrast, AI methods leverage advanced deep learning models, achieving significant improvements in automation and robustness.

CONCLUSIONS: Recent advances in CBCT and IOS registration technologies have considerably increased the efficiency and accuracy of 3D dental modelling, and these technologies show promise for application in orthodontics, implantology, and oral surgery. Geometry-based algorithms deliver reliable performance with low computational demand, whereas AI-driven approaches demonstrate significant potential for achieving fully automated and highly accurate registration. Future research should focus on challenges such as unstable registration landmarks or limited dataset diversity, to ensure their stability in complex clinical scenarios.

PMID:39878846 | DOI:10.1007/s00784-025-06183-x

Bioresour Bioprocess. 2025 Jan 29;12(1):7. doi: 10.1186/s40643-024-00835-8.

ABSTRACT

Feruloyl esterases (FEs, EC 3.1.1.73) play a crucial role in biological synthesis and metabolism. However, the identification of versatile FEs, capable of catalyzing a wide range of substrates, remains a challenge. In this study, we obtained 2085 FE sequences from the BRENDA database and initiated with an enzyme similarity network analysis, revealing three main clusters (1-3). Notably, both cluster 1 and cluster 3 included the characterized FEs, which exhibited significant differences in sequence length. Subsequent phylogenetic analysis of these clusters unveiled a correlation between phylogenetic classification and substrate promiscuity, and enzymes with broad substrate scope tended to locate within specific branches of the phylogenetic tree. Further, molecular dynamics simulations and dynamical cross-correlation matrix analysis were employed to explore structural dynamics differences between promiscuous and substrate-specific FEs. Finally, to expand the repertoire of versatile FEs, we employed deep learning models to predict potentially promiscuous enzymes and identified 38 and 75 potential versatile FEs from cluster 1 and cluster 3 with a probability score exceeding 90%. Our findings underscore the utility of integrating phylogenetic and structural features with deep learning approaches for mining versatile FEs, shedding light on unexplored enzymatic diversity and expanding the repertoire of biocatalysts for synthetic applications.

PMID:39878830 | DOI:10.1186/s40643-024-00835-8

Physiol Rep. 2025 Feb;13(3):e70187. doi: 10.14814/phy2.70187.

ABSTRACT

The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training. Eighteen healthy, young, female volunteers were randomly allocated to two groups: intervention group (IG) and control group (CG). The IG group followed an 8-week strength endurance training plan that was conducted two times per week. Before and after the training, the subjects of both groups underwent MRI scanning. The evaluation of the image data was performed by a machine learning system which is based on a 3D U-Net-based Convolutional Neural Network. The volumes of muscle, bone, and SAT were each examined using a 2 (GROUP [IG vs. CG]) × 2 (TIME [pre-intervention vs. post-intervention]) analysis of variance (ANOVA) with repeated measures for the factor TIME. The results of the ANOVA demonstrate significant TIME × GROUP interaction effects for the muscle volume (F1,16 = 12.80, p = 0.003, ηP 2 = 0.44) with an increase of 2.93% in the IG group and no change in the CG (-0.62%, p = 0.893). There were no significant changes in bone or SAT volume between the groups. This study supports the use of artificial intelligence systems to analyze MRI images as a reliable tool for monitoring training responses on body composition.

PMID:39878619 | DOI:10.14814/phy2.70187

Med Phys. 2025 Jan 29. doi: 10.1002/mp.17642. Online ahead of print.

ABSTRACT

BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.

PURPOSE: This study aims to build a deep learning based SEG framework, which leverages anatomical prior knowledge of coronary artery and calcification, to improve the SEG accuracy. Moreover, based on the SEG results, this study also try to reveal the predictive ability of the volume ratio of coronary artery and calcification for rotational atherectomy (RA).

METHODS: We present a new SEG framework, which is composed of four modules: the variational autoencoder based centerline extraction (CE) module, the self-attention (SA) module, the logic operation (LO) module, and the SEG module. Specifically, the CE module is used to crop a series of 3D CTA patches along the coronary artery, from which the continuous property of vessels can be utilized by the SA module to produce vessel-related features. According to the spatial relations between coronary artery lumen and calcification regions, the LO module with logic union and intersection is designed to refine the vessel-related features into lumen- and calcification-related features, based on which SEG results can be produced by the SEG module.

RESULTS: Experimental results demonstrate that our framework outperforms the state-of-the-art methods on CTA image dataset of 72 patients with statistical significance. Ablation experiments confirm that the proposed modules have positive impacts to the SEG results. Moreover, based on the volume ratio of segmented coronary artery and calcification, the prediction accuracy of RA is 0.75.

CONCLUSIONS: Integrating anatomical prior knowledge of coronary artery and calcification into the deep learning based SEG framework can effectively enhance the performance. Moreover, the volume ratio of segmented coronary artery and calcification is a good predictive factor for RA.

PMID:39878608 | DOI:10.1002/mp.17642

Med Phys. 2025 Jan 29. doi: 10.1002/mp.17635. Online ahead of print.

ABSTRACT

BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence. However, their clinical adoption is hindered by a lack of explainability, limiting understanding and trust in their diagnostic decisions.

PURPOSE: To utilize a novel neural ordinary differential equation (NODE) model for discerning BM post-SRS radionecrosis from recurrence. This approach integrates image-deep features, genomic biomarkers, and non-image clinical parameters within a synthesized latent feature space. The trajectory of each data sample towards the diagnosis decision can be visualized within this feature space, offering a new angle on radiogenomic data analysis foundational for AI explainability.

METHODS: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel model based on heavy ball NODE (HBNODE) in which deep feature extraction was governed by a second-order ODE. This approach enabled tracking of deep neural network (DNN) behavior by solving the HBNODE and observing the stepwise derivative evolution. Consequently, the trajectory of each sample within the Image-Genomic-Clinical (I-G-C) space became traceable. A decision-making field (F) was reconstructed within the feature space, with its gradient vectors directing the data samples' trajectories and intensities showing the potential. The evolution of F reflected the cumulative feature contributions at intermediate states to the final diagnosis, enabling quantitative and dynamic comparisons of the relative contribution of each feature category over time. A velocity curve was designed to determine key intermediate states (locoregional ∇F = 0) that are most predictive. Subsequently, a non-parametric model aggregated the optimal solutions from these key states to predict outcomes. Our dataset included 90 BMs from 62 NSCLC patients, and 3-month post-SRS T1+c MR image features, seven NSCLC genomic features, and seven clinical features were analyzed. An 8:2 train/test assignment was employed, and five independent models were trained to ensure robustness. Performance was benchmarked in sensitivity, specificity, accuracy, and ROCAUC, and results were compared against (1) a DNN using only image-based features, and (2) a combined "I+G+C" features without the HBNODE model.

RESULTS: The temporal evolution of gradient vectors and potential fields in F suggested that clinical features contribute the most during the initial stages of the HBNODE implementation, followed by imagery features taking dominance in the latter ones, while genomic features contribute the least throughout the process. The HBNODE model successfully identified and assembled key intermediate states, exhibiting competitive performance with an ROCAUC of 0.88 ± 0.04, sensitivity of 0.79 ± 0.02, specificity of 0.86 ± 0.01, and accuracy of 0.84 ± 0.01, where the uncertainties represent standard deviations. For comparison, the image-only DNN model achieved an ROCAUC of 0.71 ± 0.05 and sensitivity of 0.66 ± 0.32 (p = 0.086), while the "I+G+C" model without HBNODE reported an ROCAUC of 0.81 ± 0.02 and sensitivity of 0.58 ± 0.11 (p = 0.091).

CONCLUSION: The HBNODE model effectively identifies BM radionecrosis from recurrence, enhancing explainability within XAI frameworks. Its performance encourages further exploration in clinical settings and suggests potential applicability across various XAI domains.

PMID:39878595 | DOI:10.1002/mp.17635

J Chem Inf Model. 2025 Jan 29. doi: 10.1021/acs.jcim.4c01338. Online ahead of print.

ABSTRACT

Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification. We evaluated peptide embeddings generated from BEPLER, CPCProt, SeqVec, various ESM variants (ESM, ESM-2 with expanded feature set, ESM-1b, and ESM-1v), ProtT5-XL UniRef50, ProtT5-XL BFD, and ProtBERT. We developed pLM4CCPs, a novel deep learning architecture using convolutional neural networks (CNNs) as the classifier for binary classification of CPPs. pLM4CCPs demonstrated superior performance over existing state-of-the-art CPP prediction models, achieving improvements in accuracy (ACC) by 4.9-5.5%, Matthews correlation coefficient (MCC) by 9.3-10.2%, and sensitivity (Sn) by 14.1-19.6%. Among all the tested models, ESM-1280 and ProtT5-XL BFD demonstrated the highest overall performance on the kelm data set. ESM-1280 achieved an ACC of 0.896, an MCC of 0.796, a Sn of 0.844, and a specificity (Sp) of 0.978. ProtT5-XL BFD exhibited superior performance with an ACC of 0.901, an MCC of 0.802, an Sn of 0.885, and an Sp of 0.917. pLM4CCPs combine predictions from multiple models to provide a consensus on whether a given peptide sequence is classified as a CPP or non-CPP. This approach will enhance prediction reliability by leveraging the strengths of each individual model. A user-friendly web server for bioactivity predictions, along with data sets, is available at https://ry2acnp6ep.us-east-1.awsapprunner.com. The source code and protocol for adapting pLM4CPPs can be accessed on GitHub at https://github.com/drkumarnandan/pLM4CPPs. This platform aims to advance CPP prediction and peptide functionality modeling, aiding researchers in exploring peptide functionality effectively.

PMID:39878455 | DOI:10.1021/acs.jcim.4c01338

Int J Gynecol Cancer. 2025 Jan;35(1):100017. doi: 10.1016/j.ijgc.2024.100017. Epub 2024 Dec 17.

ABSTRACT

OBJECTIVE: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.

METHODS: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus. The deep learning method was trained using sagittal T2-weighted images from 142 patients and tested with a 3-fold stratified test with 36 patients in each fold. Our solution is based on a segmentation module, which employed a 2-stage pipeline for efficient uterus in the whole MRI volume and then tumor segmentation in the uterus predicted region of interest.

RESULTS: A total of 178 patients were included. For deep myometrial invasion prediction, the model achieved an average balanced test accuracy over 3-folds of 0.702, while experts reached an average accuracy of 0.769. For cervical stroma invasion prediction, our model demonstrated an average balanced accuracy of 0.721 on the 3-fold test set, while experts achieved an average balanced accuracy of 0.859. Additionally, the accuracy rates for uterus and tumor segmentation, measured by the Dice score, were 0.847 and 0.579 respectively.

CONCLUSION: Despite the current challenges posed by variations in data, class imbalance, and the presence of artifacts, our fully automatic approach holds great promise in supporting in pre-operative staging. Moreover, it demonstrated a robust capability to segment key regions of interest, specifically the uterus and tumors, highlighting the positive impact our solution can bring to health care imaging.

PMID:39878275 | DOI:10.1016/j.ijgc.2024.100017

ACS Electrochem. 2024 Oct 3;1(1):52-62. doi: 10.1021/acselectrochem.4c00014. eCollection 2025 Jan 2.

ABSTRACT

In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within cyclic voltammograms of multiple redox events. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers' productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory.

PMID:39878149 | PMC:PMC11728721 | DOI:10.1021/acselectrochem.4c00014

J Chem Inf Model. 2025 Jan 29. doi: 10.1021/acs.jcim.4c01893. Online ahead of print.

ABSTRACT

Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding. To overcome the above-mentioned problems, we propose an interpretable deep learning model called MutualDTA for predicting DTA. MutualDTA leverages the power of pretrained models to obtain accurate representations of drugs and targets. It also employs well-designed modules to extract hidden features from these representations. Furthermore, the interpretability of MutualDTA is realized by the Mutual-Attention module, which (i) establishes relationships between drugs and proteins from the perspective of intermolecular interactions between drug atoms and protein amino acid residues and (ii) allows MutualDTA to capture the binding sites based on attention scores. The test results on two benchmark data sets show that MutualDTA achieves the best performance compared to the 12 state-of-the-art models. Attention visualization experiments show that MutualDTA can capture partial interaction sites, which not only helps drug developers reduce the search space for binding sites, but also demonstrates the interpretability of MutualDTA. Finally, the trained MutualDTA is applied to screen high-affinity drug screens targeting Alzheimer's disease (AD)-related proteins, and the screened drugs are partially present in the anti-AD drug library. These results demonstrate the reliability of MutualDTA in drug development.

PMID:39878060 | DOI:10.1021/acs.jcim.4c01893

iScience. 2024 Dec 28;28(2):111707. doi: 10.1016/j.isci.2024.111707. eCollection 2025 Feb 21.

ABSTRACT

Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding de novo variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals. Predicted impact was compared between participants with CHD and a jointly called cohort without CHD. We then assessed the relationship of the predicted impact of ncDNVs with their sulcal folding patterns. ncDNVs predicted to increase H3K9me2 modification were associated with larger disruptions in right parietal sulcal patterns in the CHD cohort. Genes predicted to be regulated by these ncDNVs were enriched for functions related to neuronal development. This highlights the potential of deep learning models to generate hypotheses about the role of noncoding variants in brain development.

PMID:39877905 | PMC:PMC11772982 | DOI:10.1016/j.isci.2024.111707

Cureus. 2024 Dec 28;16(12):e76530. doi: 10.7759/cureus.76530. eCollection 2024 Dec.

ABSTRACT

Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images. Our AI-based framework enhances workflow efficiency and reduces human errors. This study is an initial step towards fully automating orthodontic diagnosis and treatment planning systems, specifically focusing on the automation of orthodontic diagnostic record classification using AI. Methods This study employed a dataset comprising 61,842 images collected from three dental clinics, distributed across 13 categories. A sequential classification approach was developed, starting with a primary model that categorized images into three main groups: extraoral, intraoral, and radiographic. Secondary models were applied within each group to perform the final classification. The proposed model, enhanced with attention modules, was trained and compared with pre-trained models such as ResNet50 (Microsoft Corporation, Redmond, Washington, United States) and InceptionV3 (Google LLC, Mountain View, California, United States). External validation was performed using 13,729 new samples to assess the artificial intelligence (AI) system's accuracy and generalizability compared to expert assessments. Results The deep learning framework achieved an accuracy of 99.24% on an external validation set, demonstrating performance almost on par with human experts. Additionally, the model demonstrated significantly faster processing times compared to manual methods. Gradient-weighted class activation mapping (Grad-CAM) visualizations confirmed that the model effectively focused on clinically relevant features during classification, further supporting its clinical applicability. Conclusion This study introduces a deep learning framework for automating the classification and archiving of orthodontic diagnostic images. The model achieved impressive accuracy and demonstrated clinically relevant feature focus through Grad-CAM visualizations. Beyond its high accuracy, the framework offers significant improvements in processing speed, making it a viable tool for real-time applications in orthodontics. This approach not only reduces the workload in healthcare settings but also lays the foundation for future automated diagnostic and treatment planning systems in digital orthodontics.

PMID:39877794 | PMC:PMC11774544 | DOI:10.7759/cureus.76530

Neurooncol Adv. 2025 Jan 20;7(1):vdae225. doi: 10.1093/noajnl/vdae225. eCollection 2025 Jan-Dec.

ABSTRACT

BACKGROUND: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.

METHODS: A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.76 ± 13.74 years; 136 males, 82 females), 514 patients with brain metastases (mean age 59.28 ± 12.36 years; 228 males, 286 females), 366 patients with inflammatory lesions (mean age 41.94 ± 14.57 years; 142 males, 224 females), 99 intracerebral hemorrhages (mean age 62.68 ± 16.64 years; 56 males, 43 females), and 83 meningiomas (mean age 63.99 ± 13.31 years; 25 males, 58 females). Radiomic features were extracted from fluid-attenuated inversion recovery (FLAIR), contrast-enhanced, and noncontrast T1-weighted MR sequences. Subcohorts, with 80% for training and 20% for testing, were established for model validation. Machine learning models, primarily XGBoost, were trained to distinguish gliomas from other pathologies.

RESULTS: The study demonstrated promising results in distinguishing gliomas from various intracranial pathologies. The best-performing model consistently achieved high area-under-the-curve (AUC) values, indicating strong discriminatory power across multiple distinctions, including gliomas versus metastases (AUC = 0.96), gliomas versus inflammatory lesions (AUC = 1.0), gliomas versus intracerebral hemorrhages (AUC = 0.99), gliomas versus meningiomas (AUC = 0.98). Additionally, across all these entities, gliomas had an AUC of 0.94.

CONCLUSIONS: The study presents an automated approach that effectively distinguishes gliomas from common intracranial pathologies. This can serve as a quality control upstream to further artificial-intelligence-based genetic analysis of cerebral gliomas.

PMID:39877747 | PMC:PMC11773384 | DOI:10.1093/noajnl/vdae225

MethodsX. 2025 Jan 6;14:103158. doi: 10.1016/j.mex.2025.103158. eCollection 2025 Jun.

ABSTRACT

Integrated Circuits are made of various transistors that are embedded on a silicon wafer, these wafers are difficult to process and hence are prone to defects. Defecting these defects manually is a time consuming and labour-intensive task and hence automation is necessary. Deep Learning approach is better suited in this case as it is able to generalize defects if trained properly and can be a solution to segmentation and classification of defects automatically. The segmentation model mentioned in this study achieved a Mean Absolute Error (MAE) of 0.0036, a Root Mean Squared Error (RMSE) of 0.0576, a Dice Index (DSC) of 0.7731, and an Intersection over Union (IoU) of 0.6590. The classification model achieved 0.9705 Accuracy, 0.9678 Precision, 0.9705 Recall, and 0.9676 F1 Score. In order to make this process a more interactive, an LLM with Q&A capabilities was integrated to solve any doubts and answer any questions regarding defects in wafers. This approach helps automate the detection process thus improving quality of end product.•Successful and precise defect segmentation and classification using Deep Learning was achieved.•High-intensity regions after post-processing.•An LLM offering defect analysis and guidance was streamlined.

PMID:39877475 | PMC:PMC11773255 | DOI:10.1016/j.mex.2025.103158

Ophthalmol Sci. 2024 Nov 28;5(2):100664. doi: 10.1016/j.xops.2024.100664. eCollection 2025 Mar-Apr.

ABSTRACT

OBJECTIVE: Detecting and measuring changes in longitudinal fundus imaging is key to monitoring disease progression in chronic ophthalmic diseases, such as glaucoma and macular degeneration. Clinicians assess changes in disease status by either independently reviewing or manually juxtaposing longitudinally acquired color fundus photos (CFPs). Distinguishing variations in image acquisition due to camera orientation, zoom, and exposure from true disease-related changes can be challenging. This makes manual image evaluation variable and subjective, potentially impacting clinical decision-making. We introduce our deep learning (DL) pipeline, "EyeLiner," for registering, or aligning, 2-dimensional CFPs. Improved alignment of longitudinal image pairs may compensate for differences that are due to camera orientation while preserving pathological changes.

DESIGN: EyeLiner registers a "moving" image to a "fixed" image using a DL-based keypoint matching algorithm.

PARTICIPANTS: We evaluate EyeLiner on 3 longitudinal data sets: Fundus Image REgistration (FIRE), sequential images for glaucoma forecast (SIGF), and our internal glaucoma data set from the Colorado Ophthalmology Research Information System (CORIS).

METHODS: Anatomical keypoints along the retinal blood vessels were detected from the moving and fixed images using a convolutional neural network and subsequently matched using a transformer-based algorithm. Finally, transformation parameters were learned using the corresponding keypoints.

MAIN OUTCOME MEASURES: We computed the mean distance (MD) between manually annotated keypoints from the fixed and the registered moving image. For comparison to existing state-of-the-art retinal registration approaches, we used the mean area under the curve (AUC) metric introduced in the FIRE data set study.

RESULTS: EyeLiner effectively aligns longitudinal image pairs from FIRE, SIGF, and CORIS, as qualitatively evaluated through registration checkerboards and flicker animations. Quantitative results show that the MD decreased for this model after alignment from 321.32 to 3.74 pixels for FIRE, 9.86 to 2.03 pixels for CORIS, and 25.23 to 5.94 pixels for SIGF. We also obtained an AUC of 0.85, 0.94, and 0.84 on FIRE, CORIS, and SIGF, respectively, beating the current state-of-the-art SuperRetina (AUCFIRE = 0.76, AUCCORIS = 0.83, AUCSIGF = 0.74).

CONCLUSIONS: Our pipeline demonstrates improved alignment of image pairs in comparison to the current state-of-the-art methods on 3 separate data sets. We envision that this method will enable clinicians to align image pairs and better visualize changes in disease over time.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:39877463 | PMC:PMC11773051 | DOI:10.1016/j.xops.2024.100664