Thyroid nodules are a common clinical concern, with accurate diagnosis being critical for effective treatment and improved patient outcomes. Traditional ultrasound examinations rely heavily on the physician's experience, which can lead to diagnostic variability. The integration of artificial intelligence (AI) into medical imaging offers a promising solution for enhancing diagnostic accuracy and efficiency. This study aimed to evaluate the effectiveness of the You Only Look Once v. 11 (YOLOv11) model in detecting and classifying thyroid nodules through ultrasound images, with the goal of supporting real-time clinical decision-making and improving diagnostic workflows. We used the YOLOv11 model to analyze a dataset of 1,503 thyroid ultrasound images, divided into training (1,203 images), validation (150 images), and test (150 images) sets, comprising 742 benign and 778 malignant nodules. Advanced data augmentation and transfer learning techniques were applied to optimize model performance. Comparative analysis was conducted with other YOLO variants (YOLOv3 to YOLOv10) and residual network 50 (ResNet50) to assess their diagnostic capabilities. The YOLOv11 model exhibited superior performance in thyroid nodule detection as compared to other YOLO variants (from YOLOv3 to YOLOv10) and ResNet50. At an intersection over union (IoU) of 0.5, YOLOv11 achieved a precision (P) of 0.841 and recall (R) of 0.823, outperforming ResNet50's P of 0.8333 and R of 0.8025. Among the YOLO variants, YOLOv11 consistently achieved the highest P and R values. For benign nodules, YOLOv11 obtained a P of 0.835 and R of 0.833, while for malignant nodules, it reached a P of 0.846 and a R of 0.813. Within the YOLOv11 model itself, performance varied across different IoU thresholds (0.25, 0.5, 0.7, and 0.9). Lower IoU thresholds generally resulted in better performance metrics, with P and R values decreasing as the IoU threshold increased. YOLOv11 proved to be a powerful tool for thyroid nodule detection and malignancy classification, offering high P and real-time performance. These attributes are vital for dynamic ultrasound examinations and enhancing diagnostic efficiency. Future research will focus on expanding datasets and validating the model's clinical utility in real-time settings.

Accurate staging of hepatic fibrosis is critical for prognostication and management among patients with chronic liver disease, and noninvasive, efficient alternatives to biopsy are urgently needed. This study aimed to evaluate the performance of an automated deep learning (DL) algorithm for fibrosis staging and for differentiating patients with hepatic fibrosis from healthy individuals via magnetic resonance (MR) images with and without additional clinical data. A total of 500 patients from two medical centers were retrospectively analyzed. DL models were developed based on delayed-phase MR images to predict fibrosis stages. Additional models were constructed by integrating the DL algorithm with nonimaging variables, including serologic biomarkers [aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4)], viral status (hepatitis B and C), and MR scanner parameters. Diagnostic performance, was assessed via the area under the receiver operating characteristic curve (AUROC), and comparisons were through use of the DeLong test. Sensitivity and specificity of the DL and full models (DL plus all clinical features) were compared with those of experienced radiologists and serologic biomarkers via the McNemar test. In the test set, the full model achieved AUROC values of 0.99 [95% confidence interval (CI): 0.94-1.00], 0.98 (95% CI: 0.93-0.99), 0.90 (95% CI: 0.83-0.95), 0.81 (95% CI: 0.73-0.88), and 0.84 (95% CI: 0.76-0.90) for staging F0-4, F1-4, F2-4, F3-4, and F4, respectively. This model significantly outperformed the DL model in early-stage classification (F0-4 and F1-4). Compared with expert radiologists, it showed superior specificity for F0-4 and higher sensitivity across the other four classification tasks. Both the DL and full models showed significantly greater specificity than did the biomarkers for staging advanced fibrosis (F3-4 and F4). The proposed DL algorithm provides a noninvasive method for hepatic fibrosis staging and screening, outperforming both radiologists and conventional biomarkers, and may facilitate improved clinical decision-making.

Lung cancer remains one of the malignant tumors with the highest global morbidity and mortality rates. Detecting pulmonary nodules in computed tomography (CT) images is essential for early lung cancer screening. However, traditional detection methods often suffer from low accuracy and efficiency, limiting their clinical effectiveness. This study aims to devise an advanced deep-learning framework capable of achieving high-precision, rapid identification of pulmonary nodules in CT imaging, thereby facilitating earlier and more accurate diagnosis of lung cancer. To address these issues, this paper proposes an improved deep-learning framework named YOLOv8-BCD, based on YOLOv8 and integrating the BiFormer attention mechanism, Content-Aware ReAssembly of Features (CARAFE) up-sampling method, and Depth-wise Over-Parameterized Depth-wise Convolution (DO-DConv) enhanced convolution. To overcome common challenges such as low resolution, noise, and artifacts in lung CT images, the model employs Super-Resolution Generative Adversarial Network (SRGAN)-based image enhancement during preprocessing. The BiFormer attention mechanism is introduced into the backbone to enhance feature extraction capabilities, particularly for small nodules, while CARAFE and DO-DConv modules are incorporated into the head to optimize feature fusion efficiency and reduce computational complexity. Experimental comparisons using 550 CT images from the LUng Nodule Analysis 2016 dataset (LUNA16 dataset) demonstrated that the proposed YOLOv8-BCD achieved detection accuracy and mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 (mAP_0.5) of 86.4% and 88.3%, respectively, surpassing YOLOv8 by 2.2% in accuracy, 4.5% in mAP_0.5. Additional evaluation on the external TianChi lung nodule dataset further confirmed the model's generalization capability, achieving an mAP_0.5 of 83.8% and mAP_0.5-0.95 of 43.9% with an inference speed of 98 frames per second (FPS). The YOLOv8-BCD model effectively assists clinicians by significantly reducing interpretation time, improving diagnostic accuracy, and minimizing the risk of missed diagnoses, thereby enhancing patient outcomes.

Currently, there is no fully automated tool available for evaluating the degree of cervical spinal stenosis. The aim of this study was to develop and validate the use of artificial intelligence (AI) algorithms for the assessment of cervical spinal stenosis. In this retrospective multi-center study, cervical spine magnetic resonance imaging (MRI) scans obtained from July 2020 to June 2023 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality were excluded. Sagittal T2-weighted images were used. The training data from the Fourth People's Hospital of Shanghai (Hos. 1) and Shanghai Changzheng Hospital (Hos. 2) were annotated by two musculoskeletal (MSK) radiologists following Kang's system as the standard reference. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second Transformer for classification. The performance of the deep learning (DL) model was assessed on an internal test set from Hos. 2 and an external test set from Shanghai Changhai Hospital (Hos. 3), and compared among six readers. Metrics such as detection precision, interrater agreement, sensitivity (SEN), and specificity (SPE) were calculated. Overall, 795 patients were analyzed (mean age ± standard deviation, 55±14 years; 346 female), with 589 in the training (75%) and validation (25%) sets, 206 in the internal test set, and 95 in the external test set. Four tasks with different clinical application scenarios were trained, and their accuracy (ACC) ranged from 0.8993 to 0.9532. When using a Kang system score of ≥2 as a threshold for diagnosing central cervical canal stenosis in the internal test set, both the algorithm and six readers achieved similar areas under the receiver operating characteristic curve (AUCs) of 0.936 [95% confidence interval (CI): 0.916-0.955], with a SEN of 90.3% and SPE of 93.8%; the AUC of the DL model was 0.931 (95% CI: 0.917-0.946), with a SEN in the external test set of 100%, and a SPE of 86.3%. Correlation analysis comparing the DL method, the six readers, and MRI reports between the reference standard showed a moderate correlation, with R values ranging from 0.589 to 0.668. The DL model produced approximately the same upgrades (9.2%) and downgrades (5.1%) as the six readers. The DL model could fully automatically and reliably assess cervical canal stenosis using MRI scans.

The use of multiparametric magnetic resonance imaging (MRI) in predicting lymphovascular invasion (LVI) in breast cancer has been well-documented in the literature. However, the majority of the related studies have primarily focused on intratumoral characteristics, overlooking the potential contribution of peritumoral features. The aim of this study was to evaluate the effectiveness of multiparametric MRI in predicting LVI by analyzing both intratumoral and peritumoral radiomics features and to assess the added value of incorporating both regions in LVI prediction. A total of 366 patients underwent preoperative breast MRI from two centers and were divided into training (n=208), validation (n=70), and test (n=88) sets. Imaging features were extracted from intratumoral and peritumoral T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. Five models were developed for predicting LVI status based on logistic regression: the tumor area (TA) model, peritumoral area (PA) model, tumor-plus-peritumoral area (TPA) model, clinical model, and combined model. The combined model was created incorporating the highest radiomics score and clinical factors. Predictive efficacy was evaluated via the receiver operating characteristic (ROC) curve and area under the curve (AUC). The Shapley additive explanation (SHAP) method was used to rank the features and explain the final model. The performance of the TPA model was superior to that of the TA and PA models. A combined model was further developed via multivariable logistic regression, with the TPA radiomics score (radscore), MRI-assessed axillary lymph node (ALN) status, and peritumoral edema (PE) being incorporated. The combined model demonstrated good calibration and discrimination performance across the training, validation, and test datasets, with AUCs of 0.888 [95% confidence interval (CI): 0.841-0.934], 0.856 (95% CI: 0.769-0.943), and 0.853 (95% CI: 0.760-0.946), respectively. Furthermore, we conducted SHAP analysis to evaluate the contributions of TPA radscore, MRI-ALN status, and PE in LVI status prediction. The combined model, incorporating clinical factors and intratumoral and peritumoral radscore, effectively predicts LVI and may potentially aid in tailored treatment planning.

The accurate assessment of thyroid nodules, which are increasingly common with age and lifestyle factors, is essential for early malignancy detection. Ultrasound imaging, the primary diagnostic tool for this purpose, holds promise when paired with deep learning. However, challenges persist with small datasets, where conventional data augmentation can introduce noise and obscure essential diagnostic features. To address dataset imbalance and enhance model generalization, this study integrates curriculum learning with a weakly supervised attention network to improve diagnostic accuracy for thyroid nodule classification. This study integrates curriculum learning with attention-guided data augmentation to improve deep learning model performance in classifying thyroid nodules. Using verified datasets from Siriraj Hospital, the model was trained progressively, beginning with simpler images and gradually incorporating more complex cases. This structured learning approach is designed to enhance the model's diagnostic accuracy by refining its ability to distinguish benign from malignant nodules. Among the curriculum learning schemes tested, schematic IV achieved the best results, with a precision of 100% for benign and 70% for malignant nodules, a recall of 82% for benign and 100% for malignant, and F1-scores of 90% and 83%, respectively. This structured approach improved the model's diagnostic sensitivity and robustness. These findings suggest that automated thyroid nodule assessment, supported by curriculum learning, has the potential to complement radiologists in clinical practice, enhancing diagnostic accuracy and aiding in more reliable malignancy detection.

Super-resolution deep learning reconstruction (SR-DLR) algorithm has emerged as a promising image reconstruction technique for improving the image quality of coronary computed tomography angiography (CCTA) and ensuring accurate CCTA-derived fractional flow reserve (CT-FFR) assessments even in problematic scenarios (e.g., the presence of heavily calcified plaque and stent implantation). Therefore, the purposes of this study were to evaluate the image quality of CCTA obtained with SR-DLR in comparison with conventional reconstruction methods and to investigate the diagnostic performances of different reconstruction approaches based on CT-FFR. Fifty patients who underwent CCTA and subsequent invasive coronary angiography (ICA) were retrospectively included. All images were reconstructed with hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), conventional deep learning reconstruction (C-DLR), and SR-DLR algorithms. Objective parameters and subjective scores were compared. Among the patients, 22-comprising 45 lesions-had invasive FFR results as a reference, and the diagnostic performance of different reconstruction approaches based on CT-FFR were compared. SR-DLR achieved the lowest image noise, highest signal-to-noise ratio (SNR), and best edge sharpness (all P values <0.05), as well as the best subjective scores from both reviewers (all P values <0.001). With FFR serving as a reference, the specificity and positive predictive value (PPV) were improved as compared with HIR and C-DLR (72% <i>vs.</i> 36-44% and 73% <i>vs.</i> 53-58%, respectively); moreover, SR-DLR improved the sensitivity and negative predictive value (NPV) as compared to MBIR (95% <i>vs.</i> 70% and 95% <i>vs.</i> 68%, respectively; all P values <0.05). The overall diagnostic accuracy and area under the curve (AUC) for SR-DLR were significantly higher than those of the HIR, MBIR, and C-DLR algorithms (82% <i>vs.</i> 60-67% and 0.84 <i>vs.</i> 0.61-0.70, respectively; all P values <0.05). SR-DLR had the best image quality for both objective and subjective evaluation. The diagnostic performances of CT-FFR were improved by SR-DLR, enabling more accurate assessment of flow-limiting lesions.

Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of abdominal conditions. A comprehensive assessment, especially of the liver, requires multi-planar T2-weighted sequences. To mitigate the effect of respiratory motion on image quality, the combination of acquisition and reconstruction with motion suppression (ARMS) and respiratory triggering (RT) is commonly employed. While this method maintains image quality, it does so at the expense of longer acquisition times. We evaluated the effectiveness of free-breathing, artificial intelligence-assisted compressed-sensing respiratory-triggered T2-weighted imaging (ACS-RT T2WI) compared to conventional acquisition and reconstruction with motion-suppression respiratory-triggered T2-weighted imaging (ARMS-RT T2WI) in abdominal MRI, assessing both qualitative and quantitative measures of image quality and lesion detection. In this retrospective study, 334 patients with upper abdominal discomfort were examined on a 3.0T MRI system. Each patient underwent both ARMS-RT T2WI and ACS-RT T2WI. Image quality was analyzed by two independent readers using a five-point Likert scale. The quantitative measurements included the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and sharpness. Lesion detection rates and contrast ratios (CRs) were also evaluated for liver, biliary system, and pancreatic lesions. There ACS-RT T2WI protocol had a significantly reduced median scanning time compared to the ARMS-RT T2WI protocol (148.22±38.37 <i>vs.</i> 13.86±1.72 seconds). However, ARMS-RT T2WI had a higher PSNR than ACS-RT T2WI (39.87±2.72 <i>vs.</i> 38.69±3.00, P<0.05). Of the 201 liver lesions, ARMS-RT T2WI detected 193 (96.0%) and ACS-RT T2WI detected 192 (95.5%) (P=0.787). Of the 97 biliary system lesions, ARMS-RT T2WI detected 92 (94.8%) and ACS-RT T2WI detected 94 (96.9%) (P=0.721). Of the 110 pancreatic lesions, ARMS-RT T2WI detected 102 (92.7%) and ACS-RT T2WI detected 104 (94.5%) (P=0.784). The CR analysis showed the superior performance of ACS-RT T2WI in certain lesion types (hemangioma, 0.58±0.11 <i>vs.</i> 0.55±0.12; biliary tumor, 0.47±0.09 <i>vs.</i> 0.38±0.09; pancreatic cystic lesions, 0.59±0.12 <i>vs.</i> 0.48±0.14; pancreatic cancer, 0.48±0.18 <i>vs.</i> 0.43±0.17), but no significant difference was found in others like focal nodular hyperplasia (FNH), hepatapostema, hepatocellular carcinoma (HCC), cholangiocarcinoma, metastatic tumors, and biliary calculus. ACS-RT T2WI ensures clinical reliability with a substantial scan time reduction (>80%). Despite minor losses in detail and SNR reduction, ACS-RT T2WI does not impair lesion detection, marking its efficacy in abdominal imaging.

Deep learning image reconstruction (DLIR) can enhance image quality and lower image dose, yet its impact on radiomics features (RFs) remains unclear. This study aimed to compare the effects of DLIR and conventional adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithms on the robustness of RFs using standard and low-dose abdominal clinical computed tomography (CT) scans. A total of 54 patients with hepatic masses who underwent abdominal contrast-enhanced CT scans were retrospectively analyzed. The raw data of standard dose in the venous phase and low dose in the delayed phase were reconstructed using five reconstruction settings, including ASIR-V at 30% (ASIR-V30%) and 70% (ASIR-V70%) levels, and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. The PyRadiomics platform was used for the extraction of RFs in 18 regions of interest (ROIs) in different organs or tissues. The consistency of RFs among different algorithms and different strength levels was tested by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The consistency of RFs among different strength levels of the same algorithm and clinically comparable levels across algorithms was evaluated by intraclass correlation coefficient (ICC). Robust features were identified by Kruskal-Wallis and Mann-Whitney <i>U</i> test. Among the five reconstruction methods, the mean CV and QCD in the standard-dose group were 0.364 and 0.213, respectively, and the corresponding values were 0.444 and 0.245 in the low-dose group. The mean ICC values between ASIR-V 30% and 70%, DLIR-L and M, DLIR-M and H, DLIR-L and H, ASIR-V30% and DLIR-M, and ASIR-V70% and DLIR-H were 0.672, 0.734, 0.756, 0.629, 0.724, and 0.651, respectively, in the standard-dose group, and the corresponding values were 0.500, 0.567, 0.700, 0.474, 0.499, and 0.650 in the low-dose group. The ICC values between DLIR-M and H under low-dose conditions were even higher than those of ASIR-V30% and -V70% under standard dose conditions. Among the five reconstruction settings, averages of 14.0% (117/837) and 10.3% (86/837) of RFs across 18 ROIs exhibited robustness under standard-dose and low-dose conditions, respectively. Some 23.1% (193/837) of RFs demonstrated robustness between the low-dose DLIR-M and H groups, which was higher than the 21.0% (176/837) observed in the standard-dose ASIR-V30% and -V70% groups. Most of the RFs lacked reproducibility across algorithms and energy levels. However, DLIR at medium (M) and high (H) levels significantly improved RFs consistency and robustness, even at reduced doses.

Cone-beam computed tomography (CBCT) is a three-dimensional (3D) imaging method designed for routine target verification of cancer patients during radiotherapy. The images are reconstructed from a sequence of projection images obtained by the on-board imager attached to a radiotherapy machine. CBCT images are usually stored in a health information system, but the projection images are mostly abandoned due to their massive volume. To store them economically, in this study, a deep learning (DL)-based super-resolution (SR) method for compressing the projection images was investigated. In image compression, low-resolution (LR) images were down-sampled by a factor from the high-resolution (HR) projection images and then encoded to the video file. In image restoration, LR images were decoded from the video file and then up-sampled to HR projection images via the DL network. Three SR DL networks, convolutional neural network (CNN), residual network (ResNet), and generative adversarial network (GAN), were tested along with three video coding-decoding (CODEC) algorithms: Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), and AOMedia Video 1 (AV1). Based on the two databases of the natural and projection images, the performance of the SR networks and video codecs was evaluated with the compression ratio (CR), peak signal-to-noise ratio (PSNR), video quality metric (VQM), and structural similarity index measure (SSIM). The codec AV1 achieved the highest CR among the three codecs. The CRs of AV1 were 13.91, 42.08, 144.32, and 289.80 for the down-sampling factor (DSF) 0 (non-SR) 2, 4, and 6, respectively. The SR network, ResNet, achieved the best restoration accuracy among the three SR networks. Its PSNRs were 69.08, 41.60, 37.08, and 32.44 dB for the four DSFs, respectively; its VQMs were 0.06%, 3.65%, 6.95%, and 13.03% for the four DSFs, respectively; and its SSIMs were 0.9984, 0.9878, 0.9798, and 0.9518 for the four DSFs, respectively. As the DSF increased, the CR increased proportionally with the modest degradation of the restored images. The application of the SR model can further improve the CR based on the current result achieved by the video encoders. This compression method is not only effective for the two-dimensional (2D) projection images, but also applicable to the 3D images used in radiotherapy.