BMC Med Inform Decis Mak. 2024 Oct 24;24(1):311. doi: 10.1186/s12911-024-02712-y.

ABSTRACT

Several diverse fields including the healthcare system and drug development sectors have benefited immensely through the adoption of deep learning (DL), which is a subset of artificial intelligence (AI) and machine learning (ML). Cancer makes up a significant percentage of the illnesses that cause early human mortality across the globe, and this situation is likely to rise in the coming years, especially when non-communicable illnesses are not considered. As a result, cancer patients would greatly benefit from precise and timely diagnosis and prediction. Deep learning (DL) has become a common technique in healthcare due to the abundance of computational power. Gene expression datasets are frequently used in major DL-based applications for illness detection, notably in cancer therapy. The quantity of medical data, on the other hand, is often insufficient to fulfill deep learning requirements. Microarray gene expression datasets are used for training procedures despite their extreme dimensionality, limited volume of data samples, and sparsely available information. Data augmentation is commonly used to expand the training sample size for gene data. The Wasserstein Tabular Generative Adversarial Network (WT-GAN) model is used for the data augmentation process for generating synthetic data in this proposed work. The correlation-based feature selection technique selects the most relevant characteristics based on threshold values. Deep FNN and ML algorithms train and classify the gene expression samples. The augmented data give better classification results (> 97%) when using WT-GAN for cancer diagnosis.

PMID:39449042 | DOI:10.1186/s12911-024-02712-y

Med Biol Eng Comput. 2024 Oct 25. doi: 10.1007/s11517-024-03216-7. Online ahead of print.

ABSTRACT

Ultrasound (US)-based patient-specific rupture risk analysis of abdominal aortic aneurysms (AAAs) has shown promising results. Input for these models is the patient-specific geometry of the AAA. However, segmentation of the intraluminal thrombus (ILT) remains challenging in US images due to the low ILT-blood contrast. This study aims to improve AAA and ILT segmentation in time-resolved three-dimensional (3D + t) US images using a deep learning approach. In this study a "no new net" (nnU-Net) model was trained on 3D + t US data using either US-based or (co-registered) computed tomography (CT)-based annotations. The optimal training strategy for this low-contrast data was determined for a limited dataset. The merit of augmentation was investigated, as well as the inclusion of low-contrast areas. Segmentation results were validated with CT-based geometries as the ground truth. The model trained on CT-based masks showed the best performance in terms of DICE index, Hausdorff distance, and diameter differences, covering a larger part of the AAA. With a higher accuracy and less manual input the model outperforms conventional methods, with a mean Hausdorff distance of 4.4 mm for the vessel and 7.8 mm for the lumen. However, visibility of the lumen-ILT interface remains the limiting factor, necessitating improvements in image acquisition to ensure broader patient inclusion and enable rupture risk assessment of AAAs in the future.

PMID:39448511 | DOI:10.1007/s11517-024-03216-7

Clin Oral Investig. 2024 Oct 25;28(11):610. doi: 10.1007/s00784-024-05999-3.

ABSTRACT

OBJECTIVES: Accurately identification and tooth numbering on radiographs is essential for any clinicians. The aim of the present study was to validate the hypothesis that Yolov5, a type of artificial intelligence model, can be trained to detect and number teeth in periapical radiographs.

MATERIALS AND METHODS: Six thousand four hundred forty six anonymized periapical radiographs without motion-related artifacts were randomly selected from the database. All periapical radiographs in which all boundaries of any tooth could be distinguished were included in the study. The radiographic images used were randomly divided into three groups: 80% training, 10% validation, and 10% testing. The confusion matrix was used to examine model success.

RESULTS: During the test phase, 2578 labelings were performed on 644 periapical radiographs. The number of true positive was 2434 (94.4%), false positive was 115 (4.4%), and false negative was 29 (1.2%). The recall, precision, and F1 scores were 0.9882, 0.9548, and 0.9712, respectively. Moreover, the model yielded an area under curve (AUC) of 0.603 on the receiver operating characteristic curve (ROC).

CONCLUSIONS: This study showed us that YOLOv5 is nearly perfect for numbering teeth on periapical radiography. Although high success rates were achieved as a result of the study, it should not be forgotten that artificial intelligence currently only can be guides dentists for accurate and rapid diagnosis.

CLINICAL RELEVANCE: It is thought that dentists can accelerate the radiographic examination time and inexperienced dentists can reduce the error rate by using YOLOv5. Additionally, YOLOv5 can also be used in the education of dentistry students.

PMID:39448462 | DOI:10.1007/s00784-024-05999-3

J Imaging Inform Med. 2024 Oct 24. doi: 10.1007/s10278-024-01247-y. Online ahead of print.

ABSTRACT

To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep learning to extract CXR features and a Cox proportional hazard model to generate a hazard score ("CXR-risk"). We retrospectively collected CXRs from patients visited outpatient department and physical examination center. Subsequently, we reviewed mortality and morbidity outcomes from electronic medical records. The dataset consisted of 41,945, 10,492, 31,707, and 4441 patients in the training, validation, internal test, and external test sets, respectively. During the median follow-up of 3.2 (IQR, 1.2-6.1) years of both internal and external test sets, the "CXR-risk" demonstrated C-indexes of 0.859 (95% confidence interval (CI), 0.851-0.867) and 0.870 (95% CI, 0.844-0.896), respectively. Patients with high "CXR-risk," above 85th percentile, had a significantly higher risk of mortality than those with low risk, below 50th percentile. The addition of clinical and laboratory data and radiographic report further improved the predictive accuracy, resulting in C-indexes of 0.888 and 0.900. The VAI can provide accurate predictions of mortality and morbidity outcomes using just a single CXR, and it can complement other risk prediction indicators to assist physicians in assessing patient risk more effectively.

PMID:39448455 | DOI:10.1007/s10278-024-01247-y

J Rheumatol. 2024 Oct 24:jrheum.2024-0937. doi: 10.3899/jrheum.2024-0937. Online ahead of print.

ABSTRACT

Imaging biomarkers in axial spondyloarthritis (axSpA) are currently the most specific biomarkers for the diagnosis of this condition. Despite advances in imaging, from plain radiographs-which detect only damage-to magnetic resonance imaging (MRI)-which identifies disease activity and structural change-there are still many challenges that remain. Imaging in sacroiliitis is characterized by active and structural changes. Current classification criteria stress the importance of bone marrow edema (BME); however, BME can occur in various diseases, mechanical conditions, and healthy individuals. Thus, the identification of structural lesions such as erosion, subchondral fat, backfill, and ankylosis is important to distinguish from mimics on differential diagnosis. Various imaging modalities are available to examine structural lesions, but computed tomography (CT) is considered the current reference standard. Nonetheless, recent advances in MRI allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. Therefore, the ability of MRI to detect and measure structural lesions is strengthened. Here, we present an overview of the spectrum of current and cutting-edge techniques for SpA imaging in clinical practice; namely, we discuss the advantages, disadvantages, and usefulness of imaging in SpA through radiography, low-dose and dual-energy CT, and MRI. Cutting-edge MRI sequences including volumetric interpolated breath-hold examination, ultrashort echo time, zero echo time, and deep learning-based synthetic CT that creates CT-like images without ionizing radiation, are discussed. Imaging techniques allow for quantification of inflammatory and structural lesions, which is important in the assessment of treatment response and disease progression. Radiographic damage is poorly sensitive to change. Artificial intelligence has already revolutionized radiology practice, including protocolization, image quality, and image interpretation.

PMID:39448248 | DOI:10.3899/jrheum.2024-0937

Prog Brain Res. 2024;290:141-156. doi: 10.1016/bs.pbr.2024.07.003. Epub 2024 Aug 31.

ABSTRACT

The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep learning-based approach to deidentifying facial features in brain images using a generative adversarial network to synthesize new facial features and contours. We employed the precision of the three-dimensional U-Net model to detect specific features such as the ears, nose, mouth, and eyes. Results: Our method diverges from prior studies by highlighting partial regions of the head image rather than comprehensive full-head images. We trained and tested our model on a dataset comprising 490 cases from a publicly available head computed tomography image dataset and an additional 70 cases with head MR images. Integrated data proved advantageous, with promising results. The nose, mouth, and eye detection achieved 100% accuracy, while ear detection reached 85.03% in the training dataset. In the testing dataset, ear detection accuracy was 65.98%, and the validation dataset ear detection attained 100%. Analysis of pixel value histograms demonstrated varying degrees of similarity, as measured by the Structural Similarity Index (SSIM), between raw and generated features across different facial features. The proposed methodology, tailored for partial head image processing, is well suited for real-world imaging examination scenarios and holds potential for future clinical applications contributing to the advancement of research in de-identification technologies, thus fortifying privacy safeguards.

PMID:39448110 | DOI:10.1016/bs.pbr.2024.07.003

BMJ Health Care Inform. 2024 Oct 23;31(1):e100824. doi: 10.1136/bmjhci-2023-100824.

ABSTRACT

BACKGROUND: The early detection of hypertension using simple visual images in a way that does not require physical interaction or additional devices may improve quality of care in the era of telemedicine. Pharyngeal images include vascular morphological information and may therefore be useful for identifying hypertension.

OBJECTIVES: This study sought to develop a deep learning-based artificial intelligence algorithm for identifying hypertension from pharyngeal images.

METHODS: We conducted a secondary analysis of data from a clinical trial, in which demographic information, vital signs and pharyngeal images were obtained from patients with influenza-like symptoms in multiple primary care clinics in Japan. A deep learning-based algorithm that included a multi-instance convolutional neural network was trained to detect hypertension from pharyngeal images and demographic information. The classification performance was measured by area under the receiver operating characteristic curve. Importance heatmaps of the convolutional neural network were also examined to interpret the algorithm.

RESULTS: This study included 7710 patients from 64 clinics. The training dataset comprised 6171 patients from 51 clinics (460 positive cases), and the test dataset comprised 1539 patients from 13 clinics (130 positive cases). Our algorithm achieved an area under the receiver operating characteristic curve of 0.922 (95% CI, 0.904 to 0.940), significantly improving over the baseline prediction model incorporating only demographic information, which scored 0.887 (95% CI, 0.862 to 0.911). Our algorithm had consistent classification performance across all age and sex subgroups. Importance heatmaps revealed that the algorithm focused on the posterior pharyngeal wall area, where blood vessels are mainly located.

CONCLUSIONS: The results indicate that a deep learning-based algorithm can detect hypertension with high accuracy using pharyngeal images.

PMID:39448071 | DOI:10.1136/bmjhci-2023-100824

J Dent. 2024 Oct 22:105398. doi: 10.1016/j.jdent.2024.105398. Online ahead of print.

ABSTRACT

OBJECTIVE: To establish a high-precision, automated model using deep learning for the fine classification and three-dimensional (3D) segmentation of mixed dentition in cone-beam computed tomography (CBCT) images.

METHODS: A high-precision, automated deep learning model was built based on modified nnU-Net and U-Net networks and was used to classify and segment mixed dentition. It was trained on a series of 336 CBCT scans and tested using 120 mixed dentition CBCT scans from three centers and 143 permanent dentition CBCT scans from a public dataset. The diagnostic performance of the model was assessed and compared with those of two observers with different seniority levels.

RESULTS: The model achieved accurate classification and segmentation of specific tooth positions in the internal and external mixed dentition datasets (Dice similarity coefficient: 0.964 vs. 0.951; Jaccard coefficient: 0.931 vs. 0.921; precision: 0.963 vs. 0.945; recall: 0.945 vs. 0.941; F-1 score: 0.954 vs. 0.943). These indices consistently exceeded 0.9 across multiple conditions, including fillings, malocclusion, and supernumerary tooth, with an average symmetric surface distance of 0.091 ± 0.029 mm. For permanent dentition, the Dice similarity and Jaccard coefficients exceeded 0.90, the average symmetric surface distance was 0.190 ± 0.092 mm, and precision and recall exceeded 0.94. With the aid of the model, the performance of junior dentists in mixed dentition classification and segmentation improved significantly; in contrast, there was no significant improvement in the performance of senior dentists. The speed of segmentation conducted by the dentists increased by 20.9-22.8 times.

CONCLUSION: The artificial intelligence model has strong clinical applicability, robustness, and generalizability for mixed and permanent dentition.

CLINICAL SIGNIFICANCE: The precise classification and 3D segmentation of mixed dentition in dentofacial deformities, supernumerary teeth, and metal artifacts present challenges. This study developed a deep learning approach to analyze CBCT scans, enhancing diagnostic accuracy and efficacy. It facilitates detailed measurements of tooth morphology and movement as well as informed orthodontic planning and orthotic design. Additionally, this method supports dental education by assisting doctors in explaining CBCT images to the families of pediatric patients.

PMID:39447958 | DOI:10.1016/j.jdent.2024.105398

Phys Med Biol. 2024 Oct 24. doi: 10.1088/1361-6560/ad8b09. Online ahead of print.

ABSTRACT

OBJECTIVE: Deep learning (DL) is becoming increasingly important in generating attenuation maps for accurate attenuation correction in cardiac perfusion SPECT imaging. Typically, DL models take inputs from initial reconstructed SPECT images, which are performed on the photopeak window and often also on scatter windows. While prior studies have demonstrated improvements in DL performance when scatter window images are incorporated into the DL input, the comprehensive analysis of the impact of employing different scatter windows remains unassessed. Additionally, existing research mainly focuses on applying DL to SPECT scans obtained at clinical standard count levels. This study aimed to assess utilities of DL from two aspects: 1) investigating the impact when different scatter windows were used as input to DL, and 2) evaluating the performance of DL when applied on SPECT scans acquired at a reduced count level.

APPROACH: We utilized 1517 subjects, with 386 subjects for testing and the remaining 1131 for training and validation.

MAIN RESULTS: The results showed that as scatter window width increased from 4% to 30%, a slight improvement was observed in DL estimated attenuation maps. The application of DL models to quarter-count (¼-count) SPECT scans, compared to full-count scans, showed a slight reduction in performance. Nonetheless, discrepancies across different scatter window configurations and between count levels were minimal, with all normalized mean square error (NMSE) values remaining within 2.1% when comparing the different DL attenuation maps to the reference CT maps. For attenuation corrected SPECT slices using DL estimated maps, NMSE values were within 0.5% when compared to CT correction.

SIGNIFICANCE: This study, leveraging an extensive clinical dataset, showed that the performance of DL seemed to be consistent across the use of varied scatter window settings. Moreover, our investigation into reduced count studies indicated that DL could provide accurate attenuation correction even at a ¼-count level.

PMID:39447603 | DOI:10.1088/1361-6560/ad8b09

Biomed Phys Eng Express. 2024 Oct 24. doi: 10.1088/2057-1976/ad8acb. Online ahead of print.

ABSTRACT

With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in medical image segmentation can be roughly categorized into pure convolutional networks, Transformer-based networks, and networks combining convolution and Transformer architectures. However, when dealing with complex variations and irregular shapes in medical images, existing networks face issues such as incomplete information extraction, large model parameter sizes, high computational complexity, and long processing times. In contrast, models with lower parameter counts and complexity can efficiently, quickly, and accurately identify lesion areas, significantly reducing diagnosis time and providing valuable time for subsequent treatments. Therefore, this paper proposes a lightweight network named MCI-Net, with only 5.48M parameters, a computational complexity of 4.41, and a time complexity of just 0.263. By performing linear modeling on sequences, MCI-Net permanently marks effective features and filters out irrelevant information. It efficiently captures local-global information with a small number of channels, reduces the number of parameters, and utilizes attention calculations with exchange value mapping. This achieves model lightweighting and enables thorough interaction of local-global information within the computation, establishing an overall semantic relationship of local-global information. To verify the effectiveness of the MCI-Net network, we conducted comparative experiments with other advanced representative networks on five public datasets: X-ray, Lung, ISIC-2016, ISIC-2018, and capsule endoscopy and gastrointestinal segmentation. We also performed ablation experiments on the first four datasets. The experimental results outperformed the other compared networks, confirming the effectiveness of MCI-Net. This research provides a valuable reference for achieving lightweight, accurate, and high-performance medical image segmentation network models.

PMID:39447592 | DOI:10.1088/2057-1976/ad8acb

Comput Methods Programs Biomed. 2024 Oct 11;257:108455. doi: 10.1016/j.cmpb.2024.108455. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.

METHODS: A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert-Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used.

RESULTS: The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods.

CONCLUSIONS: The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.

PMID:39447439 | DOI:10.1016/j.cmpb.2024.108455

JCO Precis Oncol. 2024 Oct;8:e2400145. doi: 10.1200/PO.24.00145. Epub 2024 Oct 24.

ABSTRACT

PURPOSE: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

MATERIALS AND METHODS: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups.

RESULTS: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.

CONCLUSION: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

PMID:39447096 | DOI:10.1200/PO.24.00145

Med Phys. 2024 Oct 24. doi: 10.1002/mp.17476. Online ahead of print.

ABSTRACT

BACKGROUND: Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy or surgery.

PURPOSE: CT imaging-based aggressiveness classification would be an important clinical advance, as it would facilitate non-invasive risk stratification and treatment planning. Here, we present a novel machine learning method, Correlated Feature Aggregation By Region (CorrFABR), for CT-based aggressiveness classification of ccRCC.

METHODS: CorrFABR is a multimodal fusion algorithm that learns from radiology and pathology images, and clinical variables in a clinically-relevant manner. CorrFABR leverages registration-independent radiology (CT) and pathology image correlations using features from vision transformer-based foundation models to facilitate aggressiveness assessment on CT images. CorrFABR consists of three main steps: (a) Feature aggregation where region-level features are extracted from radiology and pathology images at widely varying image resolutions, (b) Fusion where radiology features correlated with pathology features (pathology-informed CT biomarkers) are learned, and (c) Classification where the learned pathology-informed CT biomarkers, together with clinical variables of tumor diameter, gender, and age, are used to distinguish aggressive from indolent ccRCC using multi-layer perceptron-based classifiers. Pathology images are only required in the first two steps of CorrFABR, and are not required in the prediction module. Therefore, CorrFABR integrates information from CT images, pathology images, and clinical variables during training, but for inference, it relies solely on CT images and clinical variables, ensuring its clinical applicability. CorrFABR was trained with heterogenous, publicly-available data from 298 ccRCC tumors (136 indolent tumors, 162 aggressive tumors) in a five-fold cross-validation setup and evaluated on an independent test set of 74 tumors with a balanced distribution of aggressive and indolent tumors. Ablation studies were performed to test the utility of each component of CorrFABR.

RESULTS: CorrFABR outperformed the other classification methods, achieving an ROC-AUC (area under the curve) of 0.855 ± 0.0005 (95% confidence interval: 0.775, 0.947), F1-score of 0.793 ± 0.029, sensitivity of 0.741 ± 0.058, and specificity of 0.876 ± 0.032 in classifying ccRCC as aggressive or indolent subtypes. It was found that pathology-informed CT biomarkers learned through registration-independent correlation learning improves classification performance over using CT features alone, irrespective of the kind of features or the classification model used. Tumor diameter, gender, and age provide complementary clinical information, and integrating pathology-informed CT biomarkers with these clinical variables further improves performance.

CONCLUSION: CorrFABR provides a novel method for CT-based aggressiveness classification of ccRCC by enabling the identification of pathology-informed CT biomarkers, and integrating them with clinical variables. CorrFABR enables learning of these pathology-informed CT biomarkers through a novel registration-independent correlation learning module that considers unaligned radiology and pathology images at widely varying image resolutions.

PMID:39447001 | DOI:10.1002/mp.17476

PLoS One. 2024 Oct 24;19(10):e0310385. doi: 10.1371/journal.pone.0310385. eCollection 2024.

ABSTRACT

Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our research comprises two main parts: the first involves preprocessing the large raw AIS dataset to extract features, and the second focuses on trajectory prediction. We emphasize a specialized preprocessing approach tailored for AIS data, including advanced filtering techniques to remove outliers and erroneous data points, and the incorporation of contextual information such as environmental conditions and ship-specific characteristics. Our deep learning models utilize trajectory data sourced from the Automatic Identification System (AIS) to train and learn regular patterns within ship trajectory data, enabling them to predict trajectories for the next hour. Experimental results reveal that CNN has substantially reduced the Mean Absolute Error (MAE) and Mean Square Error (MSE) of ship trajectory prediction, showcasing superior performance compared to other deep learning algorithms. Additionally, a comparative analysis with other models-Recurrent Neural Network (RNN), GRU, LSTM, and DBS-LSTM-using metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), and Non-Linear ADE (NL-ADE), demonstrates our method's robustness and accuracy. Our approach not only cleans the data but also enriches it, providing a robust foundation for subsequent deep learning applications in ship trajectory prediction. This improvement effectively enhances the accuracy of trajectory prediction, promising advancements in maritime traffic safety.

PMID:39446863 | DOI:10.1371/journal.pone.0310385

PLoS One. 2024 Oct 24;19(10):e0308639. doi: 10.1371/journal.pone.0308639. eCollection 2024.

ABSTRACT

With the rapid development of Industrial Internet of Things (IIoT), network security issues have become increasingly severe, making intrusion detection one of the key technologies for ensuring IIoT security. However, existing intrusion detection systems face challenges such as incomplete data features, missing labels, parameter leakage, and high communication overhead. To address these challenges, this paper proposes a federated learning-based intrusion detection algorithm (NIDS-FGPA) that utilizes gradient similarity model aggregation. This algorithm leverages a federated learning architecture and combines it with Paillier homomorphic encryption technology to ensure the security of the training process. Additionally, the paper introduces the Gradient Similarity Model Aggregation (GSA) algorithm, which dynamically selects and weights updates from different models to reduce communication overhead. Finally, the paper designs a deep learning model based on two-dimensional convolutional neural networks and bidirectional gated recurrent units (2DCNN-BIGRU) to handle incomplete data features and missing labels in network traffic data. Experimental validation on the Edge-IIoTset and CIC IoT 2023 datasets achieves accuracies of 94.5% and 99.2%, respectively. The results demonstrate that the NIDS-FGPA model possesses the ability to identify and capture complex network attacks, significantly enhancing the overall security of the network.

PMID:39446819 | DOI:10.1371/journal.pone.0308639

PLoS One. 2024 Oct 24;19(10):e0312537. doi: 10.1371/journal.pone.0312537. eCollection 2024.

ABSTRACT

The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental panoramic radiographs (DPRs) were assumed as postmortem (PM) and antemortem (AM) images, respectively. The dataset contained 1,029 paired PM-AM DPRs from 2000 to 2020. After constructing a database of AM dentition, the degree of similarity was calculated and sorted in descending order. The matched rank of AM identical to an unknown PM was measured by extracting candidate groups (CGs). The percentage of rank was calculated as the success rate, and similarity scores were compared based on imaging time intervals. The matched AM images were ranked in the CG with success rates of 83.2%, 72.1%, and 59.4% in the imaging time interval for extracting the top 20.0%, 10.0%, and 5.0%, respectively. The success rates depended on sex, and were higher for women than for men: the success rates for the extraction of the top 20.0%, 10.0%, and 5.0% were 97.2%, 81.1%, and 66.5%, respectively, for women and 71.3%, 64.0%, and 52.0%, respectively, for men. The similarity score differed significantly between groups based on the imaging time interval of 17.7 years. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in effectively reducing the size of AM CG in identifying humans.

PMID:39446777 | DOI:10.1371/journal.pone.0312537

PLoS One. 2024 Oct 24;19(10):e0309430. doi: 10.1371/journal.pone.0309430. eCollection 2024.

ABSTRACT

Skin lesions encompass a variety of skin abnormalities, including skin diseases that affect structure and function, and skin cancer, which can be fatal and arise from abnormal cell growth. Early detection of lesions and automated prediction is crucial, yet accurately identifying responsible regions post-dominance dispersion remains a challenge in current studies. Thus, we propose a Convolutional Neural Network (CNN)-based approach employing a Customized Transfer Learning (CTL) model and Triple Attention (TA) modules in conjunction with Ensemble Learning (EL). While Ensemble Learning has become an integral component of both Machine Learning (ML) and Deep Learning (DL) methodologies, a specific technique ensuring optimal allocation of weights for each model's prediction is currently lacking. Consequently, the primary objective of this study is to introduce a novel method for determining optimal weights to aggregate the contributions of models for achieving desired outcomes. We term this approach "Information Gain Proportioned Averaging (IGPA)," further refining it to "Multi-Level Information Gain Proportioned Averaging (ML-IGPA)," which specifically involves the utilization of IGPA at multiple levels. Empirical evaluation of the HAM1000 dataset demonstrates that our approach achieves 94.93% accuracy with ML-IGPA, surpassing state-of-the-art methods. Given previous studies' failure to elucidate the exact focus of black-box models on specific regions, we utilize the Gradient Class Activation Map (GradCAM) to identify responsible regions and enhance explainability. Our study enhances both accuracy and interpretability, facilitating early diagnosis and preventing the consequences of neglecting skin lesion detection, thereby addressing issues related to time, accessibility, and costs.

PMID:39446759 | DOI:10.1371/journal.pone.0309430

IEEE Trans Image Process. 2024 Oct 24;PP. doi: 10.1109/TIP.2024.3483563. Online ahead of print.

ABSTRACT

Medical images often exhibit intricate structures, inhomogeneous intensity, significant noise and blurred edges, presenting challenges for medical image segmentation. Several segmentation algorithms grounded in mathematics, computer science, and medical domains have been proposed to address this matter; nevertheless, there is still considerable scope for improvement. This paper proposes a novel adaptive anatomical structure-based two-layer level set framework (AS2LS) for segmenting organs with concentric structures, such as the left ventricle and the fundus. By adaptive fitting region and edge intensity information, the AS2LS achieves high accuracy in segmenting complex medical images characterized by inhomogeneous intensity, blurred boundaries and interference from surrounding organs. Moreover, we introduce a novel two-layer level set representation based on anatomical structures, coupled with a two-stage level set evolution algorithm. Experimental results demonstrate the superior accuracy of AS2LS in comparison to representative level set methods and deep learning methods.

PMID:39446550 | DOI:10.1109/TIP.2024.3483563

IEEE Trans Vis Comput Graph. 2024 Oct 24;PP. doi: 10.1109/TVCG.2024.3485701. Online ahead of print.

ABSTRACT

Incorporating automatic style extraction and transfer from existing well-designed graph visualizations can significantly alleviate the designer's workload. There are many types of graph visualizations. In this paper, our work focuses on node-link diagrams. We present a novel approach to streamline the design process of graph visualizations by automatically extracting visual styles from well-designed examples and applying them to other graphs. Our formative study identifies the key styles that designers consider when crafting visualizations, categorizing them into global and local styles. Leveraging deep learning techniques such as saliency detection models and multi-label classification models, we develop end-to-end pipelines for extracting both global and local styles. Global styles focus on aspects such as color scheme and layout, while local styles are concerned with the finer details of node and edge representations. Through a user study and evaluation experiment, we demonstrate the efficacy and time-saving benefits of our method, highlighting its potential to enhance the graph visualization design process.

PMID:39446538 | DOI:10.1109/TVCG.2024.3485701

IEEE J Biomed Health Inform. 2024 Oct 24;PP. doi: 10.1109/JBHI.2024.3485767. Online ahead of print.

ABSTRACT

Automated polyp segmentation from colonoscopy images is crucial for colorectal cancer diagnosis. The accuracy of such segmentation, however, is challenged by two main factors. First, the variability in polyps' size, shape, and color, coupled with the scarcity of well-annotated data due to the need for specialized manual annotation, hampers the efficacy of existing deep learning methods. Second, concealed polyps often blend with adjacent intestinal tissues, leading to poor contrast that challenges segmentation models. Recently, diffusion models have been explored and adapted for polyp segmentation tasks. However, the significant domain gap between RGB-colonoscopy images and grayscale segmentation masks, along with the low efficiency of the diffusion generation process, hinders the practical implementation of these models. To mitigate these challenges, we introduce the Highlighted Diffusion Model Plus (HDM+), a two-stage polyp segmentation framework. This framework incorporates the Highlighted Diffusion Model (HDM) to provide explicit semantic guidance, thereby enhancing segmentation accuracy. In the initial stage, the HDM is trained using highlighted ground-truth data, which emphasizes polyp regions while suppressing the background in the images. This approach reduces the domain gap by focusing on the image itself rather than on the segmentation mask. In the subsequent second stage, we employ the highlighted features from the trained HDM's U-Net model as plug-in priors for polyp segmentation, rather than generating highlighted images, thereby increasing efficiency. Extensive experiments conducted on six polyp segmentation benchmarks demonstrate the effectiveness of our approach.

PMID:39446534 | DOI:10.1109/JBHI.2024.3485767