Hum Reprod Open. 2024 Nov 21;2025(1):hoae070. doi: 10.1093/hropen/hoae070. eCollection 2025.

ABSTRACT

STUDY QUESTION: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

SUMMARY ANSWER: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

WHAT IS KNOWN ALREADY: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

STUDY DESIGN SIZE DURATION: A comprehensive literature search was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to 15 May 2024. Relevant English-language studies were identified using Medical Subject Headings (MeSH) terms. We also used PubMed's 'similar articles' and 'cited by' features for thorough bibliographic screening to ensure comprehensive coverage of relevant literature.

PARTICIPANTS/MATERIALS SETTING METHODS: The review included studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes, by incorporating clinical data, hormonal levels, histopathological evaluations, and genetic parameters. Various machine learning and deep learning techniques, including logistic regression, were employed. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated the bias in the studies, and their quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines, ensuring robust reporting standards and methodological rigor.

MAIN RESULTS AND THE ROLE OF CHANCE: Out of 427 screened articles, 45 met the inclusion criteria, with most using logistic regression and machine learning to predict m-TESE outcomes. AI-based models demonstrated strong potential by integrating clinical, hormonal, and biological factors. However, limitations of the studies included small sample sizes, legal barriers, and challenges in generalizability and validation. While some studies featured larger, multicenter designs, many were constrained by sample size. Most studies had a low risk of bias in participant selection and outcome determination, and two-thirds were rated as low risk for predictor assessment, but the analysis methods varied.

LIMITATIONS REASONS FOR CAUTION: The limitations of this review include the heterogeneity of the included research, potential publication bias and reliance on only two databases (PubMed and Scopus), which may limit the scope of the findings. Additionally, the absence of a meta-analysis prevents quantitative assessment of the consistency of models. Despite this, the review offers valuable insights into AI predictive models for m-TESE in NOA.

WIDER IMPLICATIONS OF THE FINDINGS: The review highlights the potential of advanced AI techniques in predicting successful sperm retrieval for NOA patients undergoing m-TESE. By integrating clinical, hormonal, histopathological, and genetic factors, AI models can enhance decision-making and improve patient outcomes, reducing the number of unsuccessful procedures. However, to further enhance the precision and reliability of AI predictions in reproductive medicine, future studies should address current limitations by incorporating larger sample sizes and conducting prospective validation trials. This continued research and development is crucial for strengthening the applicability of AI models and ensuring broader clinical adoption.

STUDY FUNDING/COMPETING INTERESTS: The authors would like to acknowledge Mashhad University of Medical Sciences, Mashhad, Iran, for financial support (Grant ID: 4020802). The authors declare no competing interests.

REGISTRATION NUMBER: N/A.

PMID:39764557 | PMC:PMC11700607 | DOI:10.1093/hropen/hoae070

EClinicalMedicine. 2024 Nov 11;78:102919. doi: 10.1016/j.eclinm.2024.102919. eCollection 2024 Dec.

ABSTRACT

BACKGROUND: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).

METHODS: We collected video data linked to electroencephalograms (video-EEG) from infants with corrected age less than 1 year at Mount Sinai Hospital in New York City, a level four urban NICU between February 1, 2021 and December 31, 2022. We trained a deep learning pose recognition algorithm on video feeds, labeling 14 anatomic landmarks in 25 frames/infant. We then trained classifiers on anatomic landmarks to predict cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications.

FINDINGS: We built the largest video-EEG dataset to date (282,301 video minutes, 115 infants) sampled from a diverse patient population. Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants with respective receiver operating characteristic area under the curves (ROC-AUCs) 0.94, 0.83, 0.89. Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P < 5 × 10-3, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out intervals, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76).

INTERPRETATION: We show that pose AI can be applied in an ICU setting and that an EEG diagnosis, cerebral dysfunction, can be predicted from video data alone. Deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.

FUNDING: Friedman Brain Institute Fascitelli Scholar Junior Faculty Grant and Thrasher Research Fund Early Career Award (F.R.). The Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463.

PMID:39764545 | PMC:PMC11701473 | DOI:10.1016/j.eclinm.2024.102919

Anim Front. 2025 Jan 4;14(6):11-22. doi: 10.1093/af/vfae028. eCollection 2024 Dec.

NO ABSTRACT

PMID:39764526 | PMC:PMC11700597 | DOI:10.1093/af/vfae028

Anim Front. 2025 Jan 4;14(6):68-71. doi: 10.1093/af/vfae026. eCollection 2024 Dec.

NO ABSTRACT

PMID:39764520 | PMC:PMC11700576 | DOI:10.1093/af/vfae026

Natl Sci Rev. 2024 Nov 15;12(1):nwae413. doi: 10.1093/nsr/nwae413. eCollection 2025 Jan.

ABSTRACT

Affordable high-resolution cameras and state-of-the-art computer vision techniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geometric optics or marker tracking for tactile assessments, resulting in limited performance. To solve this dilemma, we introduce optical interference patterns as the visual representation of tactile information for flexible tactile sensors. We propose a novel tactile perception method and its corresponding sensor, combining structural colors from flexible blazed gratings with deep learning. The richer structural colors and finer data processing foster the tactile estimation performance. The proposed sensor has an overall normal force magnitude accuracy of 6 mN, a planar resolution of 79 μm and a contact-depth resolution of 25 μm. This work presents a promising tactile method that combines wave optics, soft materials and machine learning. It performs well in tactile measurement, and can be expanded into multiple sensing fields.

PMID:39764508 | PMC:PMC11702659 | DOI:10.1093/nsr/nwae413

Front Artif Intell. 2024 Dec 20;7:1446693. doi: 10.3389/frai.2024.1446693. eCollection 2024.

ABSTRACT

One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data. Deep learning algorithms can swiftly and effectively analyze unstructured, high-dimensional data, including texts, images, and waveforms, while advanced machine learning approaches could reveal new insights into disease risk factors and phenotypes. In summary, artificial intelligence has the potential to revolutionize various features of gastrointestinal cancer care, such as early detection, diagnosis, therapy, and prognosis. This paper highlights some of the many potential applications of artificial intelligence in this domain. Additionally, we discuss the present state of the discipline and its potential future developments.

PMID:39764458 | PMC:PMC11701808 | DOI:10.3389/frai.2024.1446693

ArXiv [Preprint]. 2024 Dec 9:arXiv:2412.06717v1.

ABSTRACT

Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance on MRAs. We curated a dataset of 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for Bankart lesion diagnosis. Separate DL models for MRAs and standard MRIs were trained using the Swin Transformer architecture, pre-trained on a public knee MRI dataset. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). Bankart lesions were identified in 31.9% of MRAs and 8.6% of standard MRIs. The models achieved AUCs of 0.87 (86% accuracy, 83% sensitivity, 86% specificity) and 0.90 (85% accuracy, 82% sensitivity, 86% specificity) on standard MRIs and MRAs, respectively. These results match or surpass radiologist performance on our dataset and reported literature metrics. Notably, our model's performance on non-invasive standard MRIs matched or surpassed the radiologists interpreting MRAs. This study demonstrates the feasibility of using DL to address the diagnostic challenges posed by subtle pathologies like Bankart lesions. Our models demonstrate potential to improve diagnostic confidence, reduce reliance on invasive imaging, and enhance accessibility to care.

PMID:39764408 | PMC:PMC11703322

ArXiv [Preprint]. 2024 Dec 24:arXiv:2412.18205v1.

ABSTRACT

Biofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed.

PMID:39764404 | PMC:PMC11703328

Sci Data. 2025 Jan 6;12(1):16. doi: 10.1038/s41597-024-04345-2.

ABSTRACT

Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.

PMID:39762327 | DOI:10.1038/s41597-024-04345-2

Sci Rep. 2025 Jan 6;15(1):970. doi: 10.1038/s41598-024-83705-9.

ABSTRACT

Bathymetric mapping of the coastal area is essential for coastal development and management. However, conventional bathymetric measurement in coastal areas is resource-expensive and under many constraints. Various research have been conducted to improve the efficiency or effectiveness of bathymetric estimations. Among them, Satellite-Derived Bathymetry (SDB) shows the greatest promise in providing a cost-effective and efficient solution due to the spatial and temporal resolution offered by satellite imagery. However, the majority of the SDB models are designed for regional bathymetry, which requires prior knowledge of the tested region. This strongly constrains their application to other regions. In this work, we present TransBathy, a deep-learning-based satellite-derived bathymetric model, to solve the coastal bathymetric mapping for different unknown challenging terrains. This model is purposefully crafted to simultaneously assimilate deep and spatial features by employing an attention mechanism. In addition, we collected a large-scale bathymetric dataset covering different shallow coastal regions across the world, including Honolulu Island, Abu Dhabi, Puerto Rico, etc. We trained the model using the collected dataset in an end-to-end manner. We validated the robustness and effectiveness of our model by conducting extensive experiments, including pre-seen and un-seen regions bathymetric estimations. When testing on pre-seen coastal regions in different locations of the world, our model achieves a good performance with an RMSE [Formula: see text] m and R2 [Formula: see text] in the depth down to [Formula: see text] m. When testing in challenging unseen coastal regions with different bottom types, our model obtains RMSE [Formula: see text] m and R2 [Formula: see text] in the steep slope region with depth down to [Formula: see text] m and obtains RMSE [Formula: see text] m and R2 [Formula: see text] in the rugged region with depth down to [Formula: see text] m. Our model even surpasses the baseline SDB method that is pre-trained in these regions by substantially reducing the RMSE by 0.978m and improving the R2 by 0.187 in the steep slope region. The dataset, code, and trained weights of the model are publicly available on GitHub.

PMID:39762308 | DOI:10.1038/s41598-024-83705-9

Zhonghua Bing Li Xue Za Zhi. 2025 Jan 8;54(1):59-65. doi: 10.3760/cma.j.cn112151-20240712-00455.

ABSTRACT

Objective: To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Methods: Specimens were collected from 209 breast cancer patients who received NAT at Renmin Hospital of Wuhan University from October 2019 to June 2023. TSR levels in pre-NAT biopsy specimens were automatically computed using a deep learning algorithm and categorized into low stroma (TSR≤30%), intermediate stroma (TSR 30% to ≤60%), and high stroma (TSR>60%) groups. Residual cancer burden (RCB) grading of post-NAT surgical specimens was determined to compare the relationship between TSR expression levels and RCB grades. The correlation of TSR with NAT efficacy was analyzed, and the association between TSR expression and patient prognosis was further investigated. Results: There were 85 cases with low stroma (TSR≤30%), 93 cases with intermediate stroma (TSR 30% to ≤60%), and 31 cases with high stroma (TSR>60%). Different TSR expression levels showed significant differences between various RCB grades (P<0.05). Logistic univariate and multivariate analyses showed that TSR was a risk factor for obtaining a complete pathological remission from neoadjuvant therapy for breast cancer when it was used as a continuous variable (P<0.05); COX regression and survival analyses showed that the lower the percentage of tumorigenic mesenchyme was, the better the prognosis of the patient was (P<0.05). Conclusions: The deep learning-based model enables automatic and accurate quantification of TSR. A lower pre-treatment tumoral stroma is associated with a lower RCB score and a higher rate of pathologic complete response, indicating that TSR can predict the efficacy of neoadjuvant therapy in breast cancer and thus holds prognostic significance. Therefore, TSR may serve as a biomarker for predicting therapeutic outcomes in breast cancer neoadjuvant therapy.

PMID:39762173 | DOI:10.3760/cma.j.cn112151-20240712-00455

Inj Prev. 2025 Jan 6:ip-2024-045314. doi: 10.1136/ip-2024-045314. Online ahead of print.

ABSTRACT

BACKGROUND: Improving the infrastructure for drug overdose surveillance is critical for identifying new threats and responding to emerging trends. We aimed to develop a prototype tool using the principles of natural language processing that can extract information from the death records of drug overdose victims.

METHODS: Data were obtained from the Violent Death Reporting System on drug overdose deaths. Narratives were manually labelled for 12 attributes of interest, totalling 82 labels about the circumstances of the overdose. Narratives were passed through the 'Excel Extractor' to identify and extract a target phrase and subsequently map the extracted phrase to predetermined code values. The output from the Excel Extractor was compared with manually labelled data to determine accuracy. Performance was compared against multiple machine learning models.

RESULTS: The Excel Extractor performed well across the attributes of interest, achieving an F1 Score over 0.8 on nine of the 12 attributes. The Excel Extractor was the highest performing model on seven of the 12 attributes. The Excel Extractor achieved an F1 Score of 0.8 or higher on 46 of 82 (56%) of the labels, and a score of 0.9 or higher on nearly one-third (25 out of 82) of the labels.

CONCLUSION: This work demonstrates it is feasible to develop a spreadsheet-formula-based natural language processing tool to accurately extract information about drug overdose deaths from narratives; for most attributes, a rule-based search performs well or better than deep learning. The Excel Extractor has the potential to streamline data abstraction for epidemiologists gathering data about drug overdose deaths.

PMID:39762007 | DOI:10.1136/ip-2024-045314

Phys Med Biol. 2025 Jan 6. doi: 10.1088/1361-6560/ada67f. Online ahead of print.

ABSTRACT

Radiation dose and diagnostic image quality are opposing constraints in x-ray CT. Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans. &#xD;Approach: To estimate organ-specific dose and noise, we compute dose maps, noise maps at desired dose levels and organ segmentations. In our pipeline, dose maps are generated using Monte Carlo simulation. The noise map is obtained by scaling the inserted noise in synthetic low-dose emulation in order to avoid anatomical structures, where the scaling coefficients are empirically calibrated. Organ segmentations are generated by a deep learning-based method (TotalSegmentator). The proposed noise model is evaluated on a clinical dataset of 12 CT scans, a phantom dataset of 3 uniform phantom scans, and a cross-site dataset of 26 scans. The accuracy of deep learning-based segmentations for organ-level dose and noise estimates was tested using a dataset of 41 cases with expert segmentations of six organs: lungs, liver, kidneys, bladder, spleen, and pancreas.&#xD;Main Results: The empirical noise model performs well, with an average RMSE approximately 1.5 HU and an average relative RMSE approximately 5% across different dose levels. The segmentation from TotalSegmentator yielded a mean Dice score of 0.8597 across the six organs (max=0.9315 in liver, min=0.6855 in pancreas). The resulting error in organ-level dose and noise estimation was less than 2% for most organs.&#xD;Significance: The proposed pipeline can output individualized organ-specific dose and noise estimates accurately for personalized protocol evaluation and optimization. It is fully automated and can be scalable to large clinical datasets. This pipeline can be used to optimize image quality for specific organs and thus clinical tasks, without adversely affecting overall radiation dose.

PMID:39761638 | DOI:10.1088/1361-6560/ada67f

BMC Med Inform Decis Mak. 2025 Jan 6;25(1):6. doi: 10.1186/s12911-024-02845-0.

ABSTRACT

BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition.

METHODS: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study. First, the Discrete Wavelet Transform (DWT) is applied to perform a five-level decomposition of the raw EEG signals, from which time-frequency and nonlinear features are extracted from the decomposed sub-bands. To eliminate redundant features, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is employed to select the most distinctive features for fusion. Finally, seizure states are classified using Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM).

RESULTS: The method was rigorously validated on the Bonn and New Delhi datasets. In the binary classification tasks, both the D-E group (Bonn dataset) and the Interictal-Ictal group (New Delhi dataset) achieved 100% accuracy, 100% sensitivity, 100% specificity, 100% precision, and 100% F1-score. In the three-class classification task A-D-E on the Bonn dataset, the model performed excellently, achieving 96.19% accuracy, 95.08% sensitivity, 97.34% specificity, 97.49% precision, and 96.18% F1-score. In addition, the proposed method was further validated on the larger and more clinically relevant CHB-MIT dataset, achieving average metrics of 98.43% accuracy, 97.84% sensitivity, 99.21% specificity, 99.14% precision, and an F1 score of 98.39%. Compared to existing literature, our method outperformed several recent studies in similar classification tasks, underscoring the effectiveness and advancement of the approach presented in this research.

CONCLUSION: The findings indicate that the proposed method demonstrates a high level of effectiveness in detecting seizures, which is a crucial aspect of managing epilepsy. By improving the accuracy of seizure detection, this method has the potential to significantly enhance the process of diagnosing and treating individuals affected by epilepsy. This advancement could lead to more tailored treatment plans, timely interventions, and ultimately, better quality of life for patients.

PMID:39762881 | DOI:10.1186/s12911-024-02845-0

J Transl Med. 2025 Jan 6;23(1):13. doi: 10.1186/s12967-024-06034-5.

ABSTRACT

BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), whereas deep learning (DL) can predict HER2 status based on tumor histopathological features. However, it remains uncertain whether these deep learning-derived features can predict the efficacy of anti-HER2 therapy.

METHODS: We analyzed a cohort of 300 consecutive surgical specimens and 101 biopsy specimens, all undergoing HER2 testing, along with 41 biopsy specimens receiving trastuzumab-based therapy for HER2-positive GAC.

RESULTS: We developed a convolutional neural network (CNN) model using surgical specimens that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification, and achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. The model also predicted HER2 status in gastric biopsy specimens, achieving an AUC of 0.723. Furthermore, our classifier was trained using 41 HER2-positive gastric biopsy specimens that had undergone trastuzumab treatment, our model demonstrated an AUC of 0.833 for the (CR + PR) / (SD + PD) subgroup.

CONCLUSION: This work explores an algorithm that utilizes hematoxylin and eosin (H&E) staining to accurately predict HER2 status and assess the response to trastuzumab in GAC, potentially facilitating clinical decision-making.

PMID:39762854 | DOI:10.1186/s12967-024-06034-5

BMC Med Imaging. 2025 Jan 6;25(1):6. doi: 10.1186/s12880-024-01522-y.

ABSTRACT

Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder's performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care.

PMID:39762786 | DOI:10.1186/s12880-024-01522-y

J Imaging Inform Med. 2025 Jan 6. doi: 10.1007/s10278-024-01372-8. Online ahead of print.

ABSTRACT

Although the relationships between basic clinical parameters and white matter hyperintensity (WMH) have been studied, the associations between vascular factors and WMH volume in general populations remain unclear. We investigated the associations between clinical parameters including comprehensive vascular factors and WMH in two large general populations. This retrospective, cross-sectional study involved two populations: individuals who underwent general health examinations at the Asan Medical Center (AMC) and participants from a regional cohort, the Korean Genome and Epidemiology Study (KoGES). WMH volume was quantified using the deep learning model nnU-Net. The associations between vascular factors and WMH volume were analyzed using multivariate linear regression. Individuals in the AMC cohort (n = 7471) had a mean [SD] age of 58.0 [9.2] years, and the KoGES participants (n = 2511), 59.2 [6.8] years. The normalized and logit-transformed WMH volumes for the AMC and KoGES were - 8.5 [1.3] and - 7.9 [1.2], respectively. The presence of carotid plaque, brachial-ankle pulse wave velocity, Agaston score, and coronary artery stenosis were associated with WMH volume after adjustments (AMC: carotid plaque β 0.13; 95% CI, 0.06-0.20; p < 0.001, baPWV β 0.001; CI 0-0.001; p < 0.001, Agaston score β 0.0003; CI 0.0001-0.0005; p < 0.001, minimal-to-mild coronary artery stenosis β 0.20; CI 0.12-0.29; p < 0.001, moderate-to-severe coronary artery stenosis β 0.30; CI 0.15-0.44; p < 0.001, KoGES: carotid plaque β 0.15; CI 0.02-0.27; p = 0.02, baPWV β 0.0004; CI 0-0.001; p = 0.001). Vascular parameters, reflecting atherosclerotic changes in carotid and coronary arteries and arterial stiffness, were independently associated with WMH volume in the general population.

PMID:39762547 | DOI:10.1007/s10278-024-01372-8

J Imaging Inform Med. 2025 Jan 6. doi: 10.1007/s10278-024-01379-1. Online ahead of print.

ABSTRACT

Nailfold microcirculation examination is crucial for the early differential diagnosis of diseases and indicating their severity. In particular, panoramic nailfold flow velocity measurements can provide direct quantitative indicators for the study of vascular diseases and technical support to assess vascular health. Previously, nailfold imaging equipment was limited by a small field of view. Therefore, research on nailfold flow velocity measurement primarily focused on improving the accuracy of single-vessel flow velocity results, while there were few studies on nailfold panoramic flow velocity. Furthermore, with improvements in the imaging field of view and the increasing clinical demand for speed in obtaining nailfold parameter results, doctors do not have time to crop videos to obtain flow velocity results. Therefore, research on nailfold panoramic flow velocity measurement is crucial. This study presents a panoramic nailfold flow velocity measurement method based on enhanced plasma gap information. In contrast to previous methods, the use of a deep learning model to decompose the panoramic flow velocity measurement task into several vessel flow velocity measurement tasks is proposed herein. For improved accuracy, a plasma gap information enhancement method is proposed, using the frame difference to enhance the position movement information of plasma gaps in videos. The t-test results show that the Pearson correlation coefficient between the results of the proposed method and those manually calculated by experts is 0.992 (t = - 0.0889, p = 0.929; > 0.05), with an average error of 2.137%. Therefore, there is no significant difference between the results obtained by the proposed method proposed and the manually calculated results. The feasibility experiment demonstrates that the proposed method can concurrently obtain the flow rate results of 13 nailfold blood vessels. Finally, the proposed method provides an efficient solution for panoramic flow velocity measurement of large-field nailfold multi-vessel videos.

PMID:39762546 | DOI:10.1007/s10278-024-01379-1

J Imaging Inform Med. 2025 Jan 6. doi: 10.1007/s10278-024-01377-3. Online ahead of print.

ABSTRACT

A scoping review was conducted to investigate the role of radiological imaging, particularly high-resolution computed tomography (HRCT), and artificial intelligence (AI) in diagnosing and prognosticating idiopathic pulmonary fibrosis (IPF). Relevant studies from the PubMed database were selected based on predefined inclusion and exclusion criteria. Two reviewers assessed study quality and analyzed data, estimating heterogeneity and publication bias. The analysis primarily focused on deep learning approaches for feature extraction from HRCT images, aiming to enhance diagnostic accuracy and efficiency. Radiomics, utilizing quantitative features extracted from images, were computed using various tools to improve precision in analysis. Validation methods such as k-fold cross-validation were employed to assess model robustness and generalizability. Findings revealed that radiologic patterns in interstitial lung disease hold prognostic significance for patient survival. However, the additional prognostic value of quantitative assessment of fibrosis extent remains uncertain. IPF poses a substantial challenge in respiratory medicine, necessitating advanced diagnostic and prognostic tools. Radiomics emerges as a valuable asset, offering insights into disease characteristics and aiding in disease classification. It contributes to understanding underlying pathophysiological processes, facilitating more effective management of pulmonary disorders. Future research should focus on clarifying the additional prognostic value of quantitative assessment and further refining AI-based diagnostic and prognostic models for IPF.

PMID:39762544 | DOI:10.1007/s10278-024-01377-3

Sci Rep. 2025 Jan 6;15(1):1027. doi: 10.1038/s41598-024-84504-y.

ABSTRACT

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements. Models incorporating attention mechanisms outperformed those without, as reflected in superior evaluation metrics. The effects of Dice Loss and Binary Cross-Entropy (BCE) Loss on the model's performance were also analyzed. Dice Loss maximized the overlap between predicted and actual segmentation masks, leading to more precise boundary delineation, while BCE Loss achieved higher recall, improving the detection of tumor areas. Grad-CAM visualizations further demonstrated that attention-based models enhanced interpretability by accurately highlighting tumor areas. The findings denote that combining advanced encoder architectures, attention mechanisms, and the UNet framework can yield more reliable and accurate breast tumor segmentation. Future research will explore the use of multi-modal imaging, real-time deployment for clinical applications, and more advanced attention mechanisms to further improve segmentation performance.

PMID:39762417 | DOI:10.1038/s41598-024-84504-y