J Clin Gastroenterol. 2025 Feb 19. doi: 10.1097/MCG.0000000000002148. Online ahead of print.

ABSTRACT

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures.

METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC).

RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively.

CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

PMID:39998988 | DOI:10.1097/MCG.0000000000002148

Eur Radiol Exp. 2025 Feb 25;9(1):29. doi: 10.1186/s41747-024-00548-9.

ABSTRACT

BACKGROUND: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery.

METHODS: Accelerated iMRI was performed using dual surface coils positioned around the area of resection. A DL model was trained on the fastMRI neuro dataset to mimic the data from the iMRI protocol. The evaluation was performed on imaging material from 40 patients imaged from Nov 1, 2021, to June 1, 2023, who underwent iMRI during tumor resection surgery. A comparative analysis was conducted between the conventional compressed sense (CS) method and the trained DL reconstruction method. Blinded evaluation of multiple image quality metrics was performed by two neuroradiologists and one neurosurgeon using a 1-to-5 Likert scale (1, nondiagnostic; 2, poor; 3, acceptable; 4, good; and 5, excellent), and the favored reconstruction variant.

RESULTS: The DL reconstruction was strongly favored or favored over the CS reconstruction for 33/40, 39/40, and 8/40 of cases for readers 1, 2, and 3, respectively. For the evaluation metrics, the DL reconstructions had a higher score than their respective CS counterparts for 72%, 72%, and 14% of the cases for readers 1, 2, and 3, respectively. Still, the DL reconstructions exhibited shortcomings such as a striping artifact and reduced signal.

CONCLUSION: DL shows promise in allowing for high-quality reconstructions of iMRI. The neuroradiologists noted an improvement in the perceived spatial resolution, signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and spatial resolution compared to CS, while the neurosurgeon preferred the CS reconstructions across all metrics.

RELEVANCE STATEMENT: DL shows promise to allow for high-quality reconstructions of iMRI, however, due to the challenging setting of iMRI, further optimization is needed.

KEY POINTS: iMRI is a surgical tool with a challenging image setting. DL allowed for high-quality reconstructions of iMRI. Additional optimization is needed due to the challenging intraoperative setting.

PMID:39998750 | DOI:10.1186/s41747-024-00548-9

J Safety Res. 2024 Dec;91:354-365. doi: 10.1016/j.jsr.2024.10.002. Epub 2024 Oct 9.

ABSTRACT

INTRODUCTION: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore, this study aims to propose a deep learning model-based multi-label risk factor recognition (MRFR) framework that automatically recognizes multiple potential material and human risk factors at construction sites. The research answers the following questions: How can a deep learning model be developed and optimized to recognize and classify multiple material and human risk factors automatically and concurrently at construction sites, and how can the decision-making process of the model be understood and improved for practical application in preemptive safety management?

METHODS: Data comprising 14,605 instances of eight types of material and human risk factors were collected from construction sites. Multiple risk factors can occur concurrently; thus, an optimal model for multi-label recognition of possible risk factors was developed.

RESULTS: The MRFR framework combines material and human risk factors into a single label while achieving satisfactory performance with an F1 score of 0.9981 and a Hamming loss of 0.0008. The causes of mispredictions by MRFR were analyzed by interpreting the decision basis of the model using visualization.

CONCLUSION: This study found that the model must have sufficient capacity to detect multiple risk factors. Performance degradation in MRFR is primarily due to difficulties recognizing visual ambiguities and a tendency to focus on nearby objects when perspective is involved.

PRACTICAL APPLICATIONS: This study contributes to safety management knowledge by developing a model to recognize multi-label material and human risk factors. Furthermore, the results can be used as guidelines for data collection methods and model improvement in the future. The MRFR framework can be used as an algorithm to recognize risk factors preemptively and automatically at real-world construction sites.

PMID:39998535 | DOI:10.1016/j.jsr.2024.10.002

Invest Ophthalmol Vis Sci. 2025 Feb 3;66(2):63. doi: 10.1167/iovs.66.2.63.

ABSTRACT

PURPOSE: To compare the prediction accuracy of brachial-ankle pulse wave velocity (baPWV) from color fundus photographs (CFPs) using different deep learning models.

METHODS: This retrospective study analyzed the data of 696 participants whose baPWVs and CFPs were obtained during medical checkups. Arteriolar and venular probability maps, which were automatically calculated from the CFPs based on our modified deep U-net, Hokkaido University retinal vessel segmentation (HURVS) model, were applied as channel attention to retinal vessel location information to predict baPWV. The baPWV prediction parameters consisted of predicted baPWVs from a single-input model using CFPs only and from a three-input model using CFPs, and arteriolar and venular probability maps. The single- and three-input models adopted a common depth-wise net and were separately pretrained and trained with fivefold cross-validation. These baPWV prediction parameters were corrected using multiple regression equations with age, sex, and systolic blood pressure and were defined as single- and three-input regression-predicted baPWVs. The main outcome measures were the correlation coefficients between true baPWV and the baPWV prediction parameters.

RESULTS: The correlation coefficient with true baPWVs was higher for the three-input predicted baPWVs (R = 0.538) than for the single-input predicted baPWVs (R = 0.527). After regression, the three-input, regression-predicted baPWVs (R = 0.704) had the highest prediction accuracy, followed by the single-input, regression-predicted baPWVs (R = 0.692).

CONCLUSIONS: The three-input model predicted true baPWVs with high accuracy. This improved prediction accuracy by channel attention to the arteriolar and venular probability maps based on the HURVS model confirmed that arterioles and venules are relevant regions for baPWV prediction.

PMID:39998460 | DOI:10.1167/iovs.66.2.63

Radiology. 2025 Feb;314(2):e240427. doi: 10.1148/radiol.240427.

ABSTRACT

Background Lutetium 177 (177Lu) prostate-specific membrane antigen (PSMA) radioligand therapy is a novel treatment option for metastatic castration-resistant prostate cancer. Evidence suggests nephrotoxicity is a delayed adverse effect in a considerable proportion of patients. Purpose To identify predictive markers for clinically significant deterioration of renal function in patients undergoing 177Lu-PSMA-I&T radioligand therapy. Materials and Methods This retrospective study analyzed patients who underwent at least four cycles of 177Lu-PSMA-I&T therapy between December 2015 and May 2022. Total kidney volume (TKV) at 3 and 6 months after treatment was extracted from CT images using TotalSegmentator, a deep learning segmentation model based on the nnU-Net framework. A decline in estimated glomerular filtration rate (eGFR) of 30% or greater was defined as clinically significant, indicating a higher risk of end-stage renal disease. Two-sided t tests and Mann-Whitney U tests were used to compare baseline nephrotoxic risk factors, changes in eGFR and TKV, prior treatments, and the number of 177Lu-PSMA-I&T cycles between patients with and without clinically significant eGFR decline at 12 months. Threshold values to differentiate between these two patient groups were identified using receiver operating characteristic curve analysis and the Youden index. Results A total of 121 patients (mean age, 76 years ± 7 [SD]) who underwent four or more cycles of 177Lu-PSMA-I&T therapy with 12 months of follow-up were included. A 10% or greater decrease in TKV at 6 months predicted 30% or greater eGFR decline at 12 months (area under the receiver operating characteristic curve, 0.90 [95% CI: 0.85, 0.96]; P < .001), surpassing other parameters. Baseline risk factors (ρ = 0.01; P = .88), prior treatments (ρ = -0.06; P = .50), and number of 177Lu-PSMA-I&T cycles (ρ = 0.08; P = .36) did not correlate with relative eGFR percentage decrease at 12 months. Conclusion Automated TKV assessment on standard-of-care CT images predicted deterioration of renal function 12 months after 177Lu-PSMA-I&T therapy initiation in metastatic castration-resistant prostate cancer. Its better performance than early relative eGFR change highlights its potential as a noninvasive marker when treatment decisions are pending. © RSNA, 2025 Supplemental material is available for this article.

PMID:39998377 | DOI:10.1148/radiol.240427

Toxins (Basel). 2025 Feb 9;17(2):78. doi: 10.3390/toxins17020078.

ABSTRACT

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom of Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools and promising candidates for therapeutic development. However, traditional conotoxin classification and functional characterization remain labor-intensive, necessitating the increasing adoption of computational approaches. In particular, machine learning (ML) techniques have facilitated advancements in sequence-based classification, functional prediction, and de novo peptide design. This review explores recent progress in applying ML and deep learning (DL) to conotoxin research, comparing key databases, feature extraction techniques, and classification models. Additionally, we discuss future research directions, emphasizing the integration of multimodal data and the refinement of predictive frameworks to enhance therapeutic discovery.

PMID:39998095 | DOI:10.3390/toxins17020078

Tomography. 2025 Jan 27;11(2):13. doi: 10.3390/tomography11020013.

ABSTRACT

BACKGROUND/OBJECTIVES: Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thickness has an adverse effect on the accuracy of the pulmonary nodule volumetry.

METHODS: We propose a deep learning based super-resolution technique to generate thin-slice CT images from thick-slice CT images. Analysis of the lung nodule volumetry and categorization accuracy was performed using commercially available AI-based lung cancer screening software.

RESULTS: The accuracy of pulmonary nodule categorization increased from 72.7 percent to 94.5 percent when thick-slice CT images were converted to generated-thin-slice CT images.

CONCLUSIONS: Applying the super-resolution-based slice generation on thick-slice CT images prior to automatic nodule evaluation significantly increases the accuracy of pulmonary nodule volumetry and corresponding pulmonary nodule category.

PMID:39997996 | DOI:10.3390/tomography11020013

Toxics. 2025 Feb 14;13(2):137. doi: 10.3390/toxics13020137.

ABSTRACT

The evaluation of air pollution is a critical concern due to its potential severe impacts on human health. Currently, vast quantities of data are collected at high frequencies, and researchers must navigate multiannual, multisite datasets trying to identify possible pollutant sources while addressing the presence of noise and sparse missing data. To address this challenge, multivariate data analysis is widely used with an increasing interest in neural networks and deep learning networks along with well-established chemometrics methods and receptor models. Here, we report a combined approach involving the Self-Organizing Map (SOM) algorithm, Hierarchical Clustering Analysis (HCA), and Positive Matrix Factorization (PMF) to disentangle multiannual, multisite data in a single elaboration without previously separating the sites and years. The approach proved to be valid, allowing us to detect the site peculiarities in terms of pollutant sources, the variation in pollutant profiles during years and the outliers, affording a reliable interpretation.

PMID:39997952 | DOI:10.3390/toxics13020137

Nurs Rep. 2025 Feb 4;15(2):55. doi: 10.3390/nursrep15020055.

ABSTRACT

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices.

PMID:39997791 | DOI:10.3390/nursrep15020055

Metabolites. 2025 Feb 14;15(2):132. doi: 10.3390/metabo15020132.

ABSTRACT

Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation.

PMID:39997757 | DOI:10.3390/metabo15020132

Metabolites. 2025 Feb 6;15(2):101. doi: 10.3390/metabo15020101.

ABSTRACT

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.

PMID:39997726 | DOI:10.3390/metabo15020101

Curr Res Struct Biol. 2025 Feb 2;9:100164. doi: 10.1016/j.crstbi.2025.100164. eCollection 2025 Jun.

ABSTRACT

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, with an increasing number of structures being determined by cryo-EM each year, many at higher resolutions. However, challenges remain in accurately interpreting cryo-EM maps. Inaccuracies can arise in regions of locally low resolution, where manual model building is more prone to errors. Validation scores for structure models have been developed to assess both the compatibility between map density and the structure, as well as the geometric and stereochemical properties of protein models. Recent advancements have introduced artificial intelligence (AI) into this field. These emerging AI-driven tools offer unique capabilities in the validation and refinement of cryo-EM-derived protein atomic models, potentially leading to more accurate protein structures and deeper insights into complex biological systems.

PMID:39996138 | PMC:PMC11848767 | DOI:10.1016/j.crstbi.2025.100164

J Exp Orthop. 2025 Feb 24;12(1):e70181. doi: 10.1002/jeo2.70181. eCollection 2025 Jan.

ABSTRACT

PURPOSE: Through an analysis of findings from a survey about the use of artificial intelligence (AI) in orthopaedics, the aim of this study was to establish a scholarly foundation for the discourse on AI in orthopaedics and to elucidate key patterns, challenges and potential future trajectories for AI applications within the field.

METHODS: The International Society of Arthroscopy, Knee Surgery and Orthopaedic Sports Medicine (ISAKOS) Young Professionals Task Force developed a survey to collect feedback on issues related to the use of AI in the orthopaedic field. The survey included 26 questions. Data obtained from the completed questionnaires were transferred to a spreadsheet and then analyzed.

RESULTS: Two hundred and eleven orthopaedic surgeons completed the survey. The survey encompassed responses from a diverse cohort of orthopaedic professionals, predominantly comprising males (92.9%). There was wide representation across all geographic regions. A notable proportion (52.1%) reported uncertainty or lack of differentiation among AI, machine learning and deep learning (47.9%). Respondents identified imaging-based diagnosis (60.2%) as the primary field of orthopaedics poised to benefit from AI. A considerable proportion (25.1%) reported using AI in their practice, with primary reasons including referencing scientific literature/publications (40.3%). The vast majority expressed interest in leveraging AI technologies (95.3%), demonstrating an inclination towards incorporating AI into orthopaedic practice. Respondents indicated specific areas of interest for further study, including prediction of patient outcomes after surgery (30.8%) and image-based diagnosis of osteoarthritis (28%).

CONCLUSIONS: This survey demonstrates that there is currently limited use of AI in orthopaedic practice, mainly due to a lack of knowledge about the subject, a lack of proven evidence of its real utility and high costs. These findings are in accordance with other surveys in the literature. However, there is also a high level of interest in its use in the future, in increased study and further research on the subject, so that it can be of real benefit and make AI an integral part of the orthopaedic surgeon's daily work.

LEVEL OF EVIDENCE: Level IV, survey study.

PMID:39996084 | PMC:PMC11848192 | DOI:10.1002/jeo2.70181

Cogn Neurodyn. 2025 Dec;19(1):44. doi: 10.1007/s11571-025-10228-y. Epub 2025 Feb 22.

ABSTRACT

Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.

PMID:39996071 | PMC:PMC11846801 | DOI:10.1007/s11571-025-10228-y

Data Brief. 2025 Jan 28;59:111342. doi: 10.1016/j.dib.2025.111342. eCollection 2025 Apr.

ABSTRACT

Language bridges the gap of communication, and Sign Language (SL) is a native language among vocal and mute community. Every region has its own sign language. In Pakistan, Urdu Sign Language (USL) is a visual gesture language used by the deaf community for communication. The Urdu alphabet in Pakistan Sign Language consists not only of static gestures but also includes dynamic gestures. There are a total of 40 alphabets in Urdu sign language, with 36 being static and 4 being dynamic. While researchers have focused on the 36 static gestures, the 4 dynamic gestures have been overlooked. Additionally, there remains a lack of advancements in the development of Pakistan Sign Language (PSL) with respect to Urdu alphabets. A dataset named UAlpa40 has been compiled, comprising 22,280 images, among which 2,897 are originally created and 19,383 are created through noise or augmentation, representing the 36 static gestures and 393 videos representing the 4 dynamic gestures, completing the set of 40 Urdu alphabets. The standard gestures for USL are published by the Family Educational Services Foundation (FESF) for the deaf and mute community of Pakistan. This dataset was prepared in real-world environments under expert supervision, with volunteers ranging from males to females aged 20 to 45. This newly developed dataset can be utilized to train vision-based deep learning models, which in turn can aid in the development of sign language translators and finger-spelling systems for USL.

PMID:39996049 | PMC:PMC11848795 | DOI:10.1016/j.dib.2025.111342

Front Psychiatry. 2025 Feb 10;16:1494369. doi: 10.3389/fpsyt.2025.1494369. eCollection 2025.

ABSTRACT

BACKGROUND: Electrical activity recorded with electroencephalography (EEG) enables the development of predictive models for emotion recognition. These models can be built using two approaches: subject-dependent and subject-independent. Although subject-independent models offer greater practical utility compared to subject-dependent models, they face challenges due to the significant variability of EEG signals between individuals.

OBJECTIVE: One potential solution to enhance subject-independent approaches is to identify EEG channels that are consistently relevant across different individuals for predicting emotion. With the growing use of deep learning in emotion recognition, incorporating attention mechanisms can help uncover these shared predictive patterns.

METHODS: This study explores this method by applying attention mechanism layers to identify EEG channels that are relevant for predicting emotions in three independent datasets (SEED, SEED-IV, and SEED-V).

RESULTS: The model achieved average accuracies of 79.3% (CI: 76.0-82.5%), 69.5% (95% CI: 64.2-74.8%) and 60.7% (95% CI: 52.3-69.2%) on these datasets, revealing that EEG channels located along the head circumference, including Fp 1, Fp 2, F 7, F 8, T 7, T 8, P 7, P 8, O 1, and O 2, are the most crucial for emotion prediction.

CONCLUSION: These results emphasize the importance of capturing relevant electrical activity from these EEG channels, thereby facilitating the prediction of emotions evoked by audiovisual stimuli in subject-independent approaches.

PMID:39995952 | PMC:PMC11847823 | DOI:10.3389/fpsyt.2025.1494369

Research (Wash D C). 2025 Feb 24;8:0615. doi: 10.34133/research.0615. eCollection 2025.

ABSTRACT

The rapid evolution of deep learning has markedly enhanced protein-biomolecule binding site prediction, offering insights essential for drug discovery, mutation analysis, and molecular biology. Advancements in both sequence-based and structure-based methods demonstrate their distinct strengths and limitations. Sequence-based approaches offer efficiency and adaptability, while structure-based techniques provide spatial precision but require high-quality structural data. Emerging trends in hybrid models that combine multimodal data, such as integrating sequence and structural information, along with innovations in geometric deep learning, present promising directions for improving prediction accuracy. This perspective summarizes challenges such as computational demands and dynamic modeling and proposes strategies for future research. The ultimate goal is the development of computationally efficient and flexible models capable of capturing the complexity of real-world biomolecular interactions, thereby broadening the scope and applicability of binding site predictions across a wide range of biomedical contexts.

PMID:39995900 | PMC:PMC11848751 | DOI:10.34133/research.0615

Front Comput Neurosci. 2025 Feb 10;19:1561635. doi: 10.3389/fncom.2025.1561635. eCollection 2025.

NO ABSTRACT

PMID:39995891 | PMC:PMC11847876 | DOI:10.3389/fncom.2025.1561635

Front Neurol. 2025 Feb 10;16:1434334. doi: 10.3389/fneur.2025.1434334. eCollection 2025.

ABSTRACT

INTRODUCTION: Although medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. Despite advances in acquisition technologies and segmentation architectures, one of the main challenges of subacute stroke lesion segmentation in computed tomography (CT) imaging is image contrast.

METHODS: To address this issue, we propose a method to assess the contrast quality of an image dataset with a ML trained model for segmentation. This method identifies the critical contrast level below which the medical-imaging model fails to learn meaningful content from images. Contrast measurement relies on the Fisher's ratio, estimating how well the stroke lesion is contrasted from the background. The critical contrast is found-thanks to the following three methods: Performance, graphical, and clustering analysis. Defining this threshold improves dataset design and accelerates training by excluding low-contrast images.

RESULTS: Application of this method to brain lesion segmentation in CT imaging highlights a Fisher's ratio threshold value of 0.05, and training validation of a new model without these images confirms this with similar results with only 60% of the training data, resulting in an almost 30% reduction in initial training time. Moreover, the model trained without the low-contrast images performed equally well with all images when tested on another database.

DISCUSSION: This study opens discussion with clinicians concerning the limitations, areas for improvement, and strategies for enhancing datasets and training models. While the methodology was only applied to stroke lesion segmentation in CT images, it has the potential to be adapted to other tasks.

PMID:39995787 | PMC:PMC11849432 | DOI:10.3389/fneur.2025.1434334

Quant Imaging Med Surg. 2025 Feb 1;15(2):1265-1274. doi: 10.21037/qims-24-772. Epub 2025 Jan 21.

ABSTRACT

BACKGROUND: Anterior compartment prolapse is a common pelvic organ prolapse (POP), which occurs frequently among middle-aged and elderly women and can cause urinary incontinence, perineal pain and swelling, and seriously affect their physical and mental health. At present, pelvic floor ultrasound is the primary examination method, but it is not carried out by many primary medical institutions due to the significant shortcomings of training in the early stage and the variable image quality. There has been great progress in the application of deep learning (DL) in image-based diagnosis in various clinical contexts. The main purpose of this study was to improve the speed and reliability of pelvic floor ultrasound diagnosis of POP by training neural networks to interpret ultrasound images, thereby facilitating the diagnosis and treatment of POP in primary care.

METHODS: This retrospective study analyzed medical records of women with anterior compartment organ prolapse (n=1,605, mean age 45.1±12.2 years) or without (n=200, mean age 38.1±13.4 years), who were examined at West China Second University Hospital between March 2019 and September 2021. Static ultrasound images of the anterior chamber of the pelvic floor (5,281 abnormal, 535 normal) were captured at rest and at maximal Valsalva motion, and four convolutional neural network (CNN) models, AlexNet, VGG-16, ResNet-18, and ResNet-50, were trained on 80% of the images, then internally validated on the other 20%. Each model was trained in two ways: through a random initialization parameter training method and through a transfer learning method based on ImageNet pre-training. The diagnostic performance of each network was evaluated according to accuracy, precision, recall and F1-score, and the receiver operating characteristic (ROC) curve of each network in the training set and validation set was drawn and the area under the curve (AUC) was obtained.

RESULTS: All four models, regardless of training method, achieved recognition accuracy of >91%, whereas transfer learning led to more stable and effective feature extraction. Specifically, ResNet-18 and ResNet-50 performed better than AlexNet and VGG-16. However, the four networks learned by transfer all showed fairly high AUCs, with the ResNet-18 network performing the best: it read images in 13.4 msec and provided recognition an accuracy of 93.53% along with an AUC of 0.852.

CONCLUSIONS: Combining DL with pelvic floor ultrasonography can substantially accelerate diagnosis of anterior compartment organ prolapse in women while improving accuracy.

PMID:39995742 | PMC:PMC11847209 | DOI:10.21037/qims-24-772