Artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommon thyroid cancer types with ultrasound, where scarce data leads to frequent misdiagnosis. Traditional augmentation strategies do not capture the unique disease variations, hindering model training and performance. To overcome this, we propose a text-driven generative method that fuses clinical insights with image generation, producing synthetic samples that realistically reflect rare subtypes. In rigorous evaluations, our approach achieves substantial gains in diagnostic metrics, surpasses existing methods in authenticity and diversity measures, and generalizes effectively to other private and public datasets with various rare cancers. In this work, we demonstrate that text-guided image augmentation substantially enhances model accuracy and robustness for rare tumor detection, offering a promising avenue for more reliable and widespread clinical adoption.

Early allograft dysfunction (EAD) significantly affects liver transplantation prognosis. This study evaluated the effectiveness of artificial intelligence (AI)-assisted methods in accurately diagnosing EAD and identifying its causes. The primary metric for assessing the accuracy was the area under the receiver operating characteristic curve (AUC). Accuracy, sensitivity, and specificity were calculated and analyzed to compare the performance of the AI models with each other and with radiologists. EAD classification followed the criteria established by Olthoff et al. A total of 582 liver transplant patients who underwent transplantation between December 2012 and June 2021 were selected. Among these, 117 patients (mean age 33.5 ± 26.5 years, 80 men) were evaluated. The ultrasound parameters, images, and clinical information of patients were extracted from the database to train the AI model. The AUC for the ultrasound-spectrogram fusion network constructed from four ultrasound images and medical data was 0.968 (95%CI: 0.940, 0.991), outperforming radiologists by 30% for all metrics. AI assistance significantly improved diagnostic accuracy, sensitivity, and specificity (P < 0.050) for both experienced and less-experienced physicians. EAD lacks efficient diagnosis and causation analysis methods. The integration of AI and ultrasound enhances diagnostic accuracy and causation analysis. By modeling only images and data related to blood flow, the AI model effectively analyzed patients with EAD caused by abnormal blood supply. Our model can assist radiologists in reducing judgment discrepancies, potentially benefitting patients with EAD in underdeveloped regions. Furthermore, it enables targeted treatment for those with abnormal blood supply.

To compare the axial cranial ultrasound images of normal and open neural tube defect (NTD) fetuses using a deep learning (DL) model and to assess its predictive accuracy in identifying open NTD.It was a prospective case-control study. Axial trans-thalamic fetal ultrasound images of participants with open fetal NTD and normal controls between 14 and 28 weeks of gestation were taken after consent. The images were divided into training, testing, and validation datasets randomly in the ratio of 70:15:15. The images were further processed and classified using DL convolutional neural network (CNN) transfer learning (TL) models. The TL models were trained for 50 epochs. The data was analyzed in terms of Cohen kappa score, accuracy score, area under receiver operating curve (AUROC) score, F1 score validity, sensitivity, and specificity of the test.A total of 59 cases and 116 controls were fully followed. Efficient net B0, Visual Geometry Group (VGG), and Inception V3 TL models were used. Both Efficient net B0 and VGG16 models gave similar high training and validation accuracy (100 and 95.83%, respectively). Using inception V3, the training and validation accuracy was 98.28 and 95.83%, respectively. The sensitivity and specificity of Efficient NetB0 was 100 and 89%, respectively, and was the best.The analysis of the changes in axial images of the fetal cranium using the DL model, Efficient Net B0 proved to be an effective model to be used in clinical application for the identification of open NTD. · Open spina bifida is often missed due to the nonrecognition of the lemon sign on ultrasound.. · Image classification using DL identified open spina bifida with excellent accuracy.. · The research is clinically relevant in low- and middle-income countries..

BackgroundLung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray and similar performance to CT at lower cost. Despite these benefits, widespread adoption is limited by operator dependency, moderate interrater reliability, and training requirements. Deep learning (DL) could potentially address these challenges, but development of effective algorithms is hindered by the scarcity of comprehensive image repositories with proper metadata. MethodsWe created an open-source dataset of LUS images derived a multi-center study involving N=226 adult patients presenting with respiratory symptoms to emergency departments between March 2020 and April 2022. Images were acquired using a standardized scanning protocol (12-zone or modified 8-zone) with various point-of-care ultrasound devices. Three blinded researchers independently analyzed each image following consensus guidelines, with disagreements adjudicated to provide definitive interpretations. Videos were pre-processed to remove identifiers, and frames were extracted and resized to 128x128 pixels. ResultsThe dataset contains 1,874 video clips comprising 303,977 frames. Half of the participants (50%) had COVID-19 pneumonia. Among all clips, 66% contained no abnormalities, 18% contained B-lines, 4.5% contained consolidations, 6.4% contained both B-lines and consolidations, and 5.2% had indeterminate findings. Pathological findings varied significantly by lung zone, with anterior zones more frequently normal and less likely to show consolidations compared to lateral and posterior zones. DiscussionThis dataset represents one of the largest annotated LUS repositories to date, including both COVID-19 and non-COVID-19 patients. The comprehensive metadata and expert interpretations enhance its utility for DL applications. Despite limitations including potential device-specific characteristics and COVID-19 predominance, this repository provides a valuable resource for developing AI tools to improve LUS acquisition and interpretation.

This paper proposes batch augmentation with unimodal fine-tuning to detect the fetus's organs from ultrasound images and associated clinical textual information. We also prescribe pre-training initial layers with investigated medical data before the multimodal training. At first, we apply a transferred initialization with the unimodal image portion of the dataset with batch augmentation. This step adjusts the initial layer weights for medical data. Then, we apply neural networks (NNs) with fine-tuned initial layers to images in batches with batch augmentation to obtain features. We also extract information from descriptions of images. We combine this information with features obtained from images to train the head layer. We write a dataloader script to load the multimodal data and use existing unimodal image augmentation techniques with batch augmentation for the multimodal data. The dataloader brings a new random augmentation for each batch to get a good generalization. We investigate the FPU23 ultrasound and UPMC Food-101 multimodal datasets. The multimodal large language model (LLM) with the proposed training provides the best results among the investigated methods. We receive near state-of-the-art (SOTA) performance on the UPMC Food-101 dataset. We share the scripts of the proposed method with traditional counterparts at the following repository: github.com/dipuk0506/multimodal

Differentiating between follicular thyroid adenoma (FTA), carcinoma (FTC), and follicular tumor with uncertain malignant potential (FT-UMP) remains challenging due to their overlapping ultrasound characteristics. This retrospective study aimed to enhance preoperative diagnostic accuracy by utilizing intra- and peritumoral radiomics based on ultrasound images. We collected post-thyroidectomy ultrasound images from 774 patients diagnosed with FTA (n = 429), FTC (n = 158), or FT-UMP (n = 187) between January 2018 and December 2023. Six peritumoral regions were expanded by 5%-30% in 5% increments, with the segment-anything model utilizing prompt learning to detect the field of view and constrain the expanded boundaries. A stepwise classification strategy addressing three tasks was implemented: distinguishing FTA from the other types (task 1), differentiating FTC from FT-UMP (task 2), and classifying all three tumors. Diagnostic models were developed by combining radiomic features from tumor and peritumoral regions with clinical characteristics. Clinical characteristics combined with intratumoral and 5% peritumoral radiomic features performed best across all tasks (Test set: area under the curves, 0.93 for task 1 and 0.90 for task 2; diagnostic accuracy, 79.9%). The DeLong test indicated that all peritumoral radiomics significantly improved intratumoral radiomics performance and clinical characteristics (p < 0.04). The 5% peritumoral regions showed the best performance, though not all results were significant (p = 0.01-0.91). Ultrasound-based intratumoral and peritumoral radiomics can significantly enhance preoperative diagnostic accuracy for FTA, FTC, and FT-UMP, leading to improved treatment strategies and patient outcomes. Furthermore, the 5% peritumoral area may indicate regions of potential tumor invasion requiring further investigation.

Three-dimensional (3D) ultrasound (US) reconstruction is of significant value in clinical diagnosis, characterized by its safety, portability, low cost, and high real-time capabilities. 3D freehand ultrasound reconstruction aims to eliminate the need for tracking devices, relying solely on image data to infer the spatial relationships between frames. However, inherent jitter during handheld scanning introduces significant inaccuracies, making current methods ineffective in precisely predicting the spatial motions of ultrasound image frames. This leads to substantial cumulative errors over long sequence modeling, resulting in deformations or artifacts in the reconstructed volume. To address these challenges, we proposed UltrasOM, a 3D ultrasound reconstruction network designed for spatial relative motion estimation. Initially, we designed a video embedding module that integrates optical flow dynamics with original static information to enhance motion change features between frames. Next, we developed a Mamba-based spatiotemporal attention module, utilizing multi-layer stacked Space-Time Blocks to effectively capture global spatiotemporal correlations within video frame sequences. Finally, we incorporated correlation loss and motion speed loss to prevent overfitting related to scanning speed and pose, enhancing the model's generalization capability. Experimental results on a dataset of 200 forearm cases, comprising 58,011 frames, demonstrated that the proposed method achieved a final drift rate (FDR) of 10.24 %, a frame-to-frame distance error (DE) of 7.34 mm, a symmetric Hausdorff distance error (HD) of 10.81 mm, and a mean angular error (MEA) of 2.05°, outperforming state-of-the-art methods by 13.24 %, 15.11 %, 3.57 %, and 6.32 %, respectively. By integrating optical flow features and deeply exploring contextual spatiotemporal dependencies, the proposed network can directly predict the relative motions between multiple frames of ultrasound images without the need for tracking, surpassing the accuracy of existing methods.

This study aims to compare the diagnostic efficiency of the American College of Radiology-Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), fine-needle aspiration (FNA) cytopathology alone, and the dynamic artificial intelligence (AI) diagnostic system. A total of 1035 patients from three hospitals were included in the study. Of these, 590 were from the retrospective dataset and 445 cases were from the prospective dataset. The diagnostic accuracy of the dynamic AI system in the thyroid nodules was evaluated in comparison to the gold standard of postoperative pathology. The sensitivity, specificity, ROC, and diagnostic differences in the κ-factor relative to the gold standard were analyzed for the AI system and the FNA. The dynamic AI diagnostic system showed good diagnostic stability in different ages and sexes and nodules of different sizes. The diagnostic AUC of the dynamic AI system showed a significant improvement from 0.89 to 0.93 compared to ACR TI-RADS. Compared to that of FNA cytopathology, the diagnostic efficacy of the dynamic AI system was found to be no statistical difference in both the retrospective cohort and the prospective cohort. The dynamic AI diagnostic system enhances the accuracy of ACR TI-RADS-based diagnoses and has the potential to replace biopsies, thus reducing the necessity for invasive procedures in patients.

Microwave thermotherapy is a promising approach for cancer treatment, but accurate noninvasive temperature monitoring remains challenging. This study aims to achieve accurate temperature prediction during microwave thermotherapy by efficiently integrating multi-feature data, thereby improving the accuracy and reliability of noninvasive thermometry techniques. We proposed an enhanced recurrent neural network architecture, namely CirnetamorNet. The experimental data acquisition system is developed by using the material that simulates the characteristics of human tissue to construct the body model. Ultrasonic image data at different temperatures were collected, and 5 parameters with high temperature correlation were extracted from gray scale covariance matrix and Homodyned-K distribution. Using multi-feature data as input and temperature prediction as output, the CirnetamorNet model is constructed by multi-head attention mechanism. Model performance was evaluated by analyzing training losses, predicting mean square error and accuracy, and ablation experiments were performed to evaluate the contribution of each module. Compared with common models, the CirnetamorNet model performs well, with training losses as low as 1.4589 and mean square error of only 0.1856. Its temperature prediction accuracy of 0.3°C exceeds that of many advanced models. Ablation experiments show that the removal of any key module of the model will lead to performance degradation, which proves that the collaboration of all modules is significant for improving the performance of the model. The proposed CirnetamorNet model exhibits exceptional performance in noninvasive thermometry for microwave thermotherapy. It offers a novel approach to multi-feature data fusion in the medical field and holds significant practical application value.

To perform a systematic review on artificial intelligence (AI) studies focused on identifying and differentiating pelvic gynecological tumors on ultrasound scans. Studies developing or validating AI models for diagnosing gynecological pelvic tumors on ultrasound scans were eligible for inclusion. We systematically searched PubMed, Embase, Web of Science, and Cochrane Central from their database inception until April 30th, 2024. To assess the quality of the included studies, we adapted the QUADAS-2 risk of bias tool to address the unique challenges of AI in medical imaging. Using multi-level random effects models, we performed a meta-analysis to generate summary estimates of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To provide a reference point of current diagnostic support tools for ultrasound examiners, we descriptively compared the pooled performance to that of the well-recognized ADNEX model on external validation. Subgroup analyses were performed to explore sources of heterogeneity. From 9151 records retrieved, 44 studies were eligible: 40 on ovarian, three on endometrial, and one on myometrial pathology. Overall, 95% were at high risk of bias - primarily due to inappropriate study inclusion criteria, the absence of a patient-level split of training and testing image sets, and no calibration assessment. For ovarian tumors, the summary AUC for AI models distinguishing benign from malignant tumors was 0.89 (95% CI: 0.85-0.92). In lower-risk studies (at least three low-risk domains), the summary AUC dropped to 0.87 (0.83-0.90), with deep learning models outperforming radiomics-based machine learning approaches in this subset. Only five studies included an external validation, and six evaluated calibration performance. In a recent systematic review of external validation studies, the ADNEX model had a pooled AUC of 0.93 (0.91-0.94) in studies at low risk of bias. Studies on endometrial and myometrial pathologies were reported individually. Although AI models show promising discriminative performances for diagnosing gynecological tumors on ultrasound, most studies have methodological shortcomings that result in a high risk of bias. In addition, the ADNEX model appears to outperform most AI approaches for ovarian tumors. Future research should emphasize robust study designs - ideally large, multicenter, and prospective cohorts that mirror real-world populations - along with external validation, proper calibration, and standardized reporting. This study was pre-registered with Open Science Framework (OSF): https://doi.org/10.17605/osf.io/bhkst.