We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function. A total of 100 patients who underwent invasive coronary angiography, OCT, and CCTA before discharge were included in this study. The data were randomly divided into a training set (80 %) and a test set (20 %). The training set, comprising 21,471 tomography images, was used to train a deep-learning convolutional neural network. Subsequently, the AI model was integrated with flow reserve score calculation software developed by Ruixin Medical. The results from the test set demonstrated excellent agreement between the AI model and OCT analysis for calcified plaque (McNemar test, p = 0.683), non-calcified plaque (McNemar test, p = 0.752), mixed plaque (McNemar test, p = 1.000), and low-attenuation plaque (McNemar test, p = 1.000). Additionally, there was excellent agreement for deep learning-derived minimum lumen diameter (intraclass correlation coefficient [ICC] 0.91, p < 0.001), mean vessel diameter (ICC 0.88, p < 0.001), and percent diameter stenosis (ICC 0.82, p < 0.001). In diagnosing >50 % coronary stenosis, the diagnostic accuracy of the AI model surpassed that of conventional CCTA (AUC 0.98 vs. 0.76, p = 0.008). When compared with quantitative flow fraction, there was excellent agreement between QFR and AI-derived CT-FFR (ICC 0.745, p < 0.0001). Our AI model effectively provides automated analysis of plaque characteristics from CCTA images, with the analysis results showing strong agreement with OCT findings. Moreover, the CT-FFR automatically analyzed by the AI model exhibits high consistency with QFR derived from coronary angiography.

To develop dynamic MRU protocol that focuses on the bladder to capture ureteral jets and to automatically estimate frequency and duration of ureteral jets from the dynamic images. Between February and July 2023, we collected 51 sets of dynamic MRU data from 5 healthy subjects. To capture the entire longitudinal trajectory of ureteral jets, we optimized orientation and thickness of the imaging slice for dynamic MRU, and developed a deep-learning method to automatically estimate frequency and duration of ureteral jets from the dynamic images. Among the 15 sets of images with different slice positioning, the positioning with slice thickness of 25 mm and orientation of 30° was optimal. Of the 36 sets of dynamic images acquired with the optimal protocol, 27 sets or 2529 images were used to train a U-Net model for automatically detecting the presence of ureteral jets. On the other 9 sets or 760 images, accuracy of the trained model was found to be 84.9 %. Based on the results of automatic detection, frequency of ureteral jet in each set of dynamic images was estimated as 8.0 ± 1.4 min^-1, deviating from reference by -3.3 % ± 10.0 %; duration of each individual ureteral jet was estimated as 7.3 ± 2.8 s, deviating from reference by 2.4 % ± 32.2 %. The accumulative duration of ureteral jets estimated by the method correlated well (with coefficient of 0.936) with the bladder expansion recorded in the dynamic images. The proposed method was capable of quantitatively characterizing ureteral jets, potentially providing valuable information on functional status of ureteral peristalsis.

This study aimed to compare the quality and readability of the responses generated by 3 publicly available artificial intelligence (AI) chatbots in answering frequently asked questions (FAQs) related to Oral and Maxillofacial Radiology (OMR) to assess their suitability for patient education. Fifteen OMR-related questions were selected from professional patient information websites. These questions were posed to ChatGPT-3.5 by OpenAI, Gemini 1.5 Pro by Google, and Copilot by Microsoft to generate responses. Three board-certified OMR specialists evaluated the responses regarding scientific adequacy, ease of understanding, and overall reader satisfaction. Readability was assessed using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) scores. The Wilcoxon signed-rank test was conducted to compare the scores assigned by the evaluators to the responses from the chatbots and professional websites. Interevaluator agreement was examined by calculating the Fleiss kappa coefficient. There were no significant differences between groups in terms of scientific adequacy. In terms of readability, chatbots had overall mean FKGL and FRE scores of 12.97 and 34.11, respectively. Interevaluator agreement level was generally high. Although chatbots are relatively good at responding to FAQs, validating AI-generated information using input from healthcare professionals can enhance patient care and safety. Readability of the text content in the chatbots and websites requires high reading levels.

To develop and validate the value of different machine learning models of pericoronary adipose tissue (PCAT) radiomics based on coronary computed tomography angiography (CCTA) for predicting coronary plaque progression (PP). This retrospective study evaluated 97 consecutive patients (with 127 plaques: 40 progressive and 87 nonprogressive) who underwent serial CCTA examinations. We analyzed conventional parameters and PCAT radiomics features. PCAT radiomics models were constructed using logistic regression (LR), K-nearest neighbors (KNN), and random forest (RF). Logistic regression analysis was applied to identify variables for developing conventional parameter models. Model performances were assessed by metrics including area under the curve (AUC), accuracy, sensitivity, and specificity. At baseline CCTA, 93 radiomics features were extracted from CCTA images. After dimensionality reduction and feature selection, two radiomics features were deemed valuable. Among radiomics models, we selected the RF as the optimal model in the training and validation sets (AUC = 0.971, 0.821). At follow-up CCTA, logistic regression analysis showed that increase in fat attenuation index (FAI) and decrease in PCAT volume were independent predictors of PP. The predictive capability of the combined model (increase in FAI + decrease in PCAT volume) was the best in the training and validation sets (AUC = 0.907, 0.882). At baseline CCTA, the RF-based PCAT radiomics model demonstrated excellent predictive ability for PP. Furthermore, at follow-up CCTA, our results indicated that both increase in FAI and decrease in PCAT volume can independently predict PP, and their combination provided enhanced predictive ability.

Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic and imaging characteristics of phase-shift nanodroplets in fibrin-based, tissue-mimicking hydrogels using passive cavitation detection and active imaging techniques, including B-mode and contrast-enhanced ultrasound. The findings demonstrated that the backscattered signal intensities and pronounced nonlinear acoustic responses, including subharmonic and higher harmonic frequencies, of ADV-generated bubbles correlated inversely with fibrin density. Additionally, we quantified the mean echo intensity, bubble cloud area, and second-order texture features of the generated ADV bubbles across varying fibrin densities. ADV bubbles in softer hydrogels displayed significantly higher mean echo intensities, larger bubble cloud areas, and more heterogeneous textures. In contrast, texture uniformity, characterized by variance, homogeneity, and energy, correlated directly with fibrin density. Furthermore, we incorporated transfer learning with convolutional neural networks, adapting AlexNet into two specialized models for differentiating fibrin hydrogels. The integration of deep learning techniques with ADV offers great potential, paving the way for future advancements in biomedical diagnostics.

Sarcopenia is associated with worse survival in non-small cell lung cancer (NSCLC), but less studied in association with toxicity. Here, we investigated the association between imaging-assessed sarcopenia with toxicity in patients with NSCLC. We analyzed a "chemoradiation" cohort (n = 318) of patients with NSCLC treated with chemoradiation, and an external validation "chemo-surgery" cohort (n = 108) who were treated with chemotherapy and surgery from 2002 to 2013 at a different institution. A deep-learning pipeline utilized pretreatment computed tomography scans to estimate SM area at the third lumbar vertebral level. Sarcopenia was defined by dichotomizing SM index, (SM adjusted for height and sex). Primary endpoint was NCI CTCAE v5.0 grade 3 to 5 (G3-5) toxicity within 21-days of first chemotherapy cycle. Multivariable analyses (MVA) of toxicity endpoints with sarcopenia and baseline characteristics were performed by logistic regression, and overall survival (OS) was analyzed using Cox regression. Sarcopenia was identified in 36% and 36% of patients in the chemoradiation and chemo-surgery cohorts, respectively. On MVA, sarcopenia was associated with worse G3-5 toxicity in chemoradiation (HR 2.00, P < .01) and chemo-surgery cohorts (HR 2.95, P = .02). In the chemoradiation cohort, worse OS was associated with G3-5 toxicity (HR 1.42, P = .02) but not sarcopenia on MVA. In chemo-surgery cohort, worse OS was associated with sarcopenia (HR 2.03, P = .02) but not G3-5 toxicity on MVA. Sarcopenia, assessed by an automated deep-learning system, was associated with worse toxicity and survival outcomes in patients with NSCLC. Sarcopenia can be utilized to tailor treatment decisions to optimize adverse events and survival.

This study set out to test the efficacy of different techniques used to manage to class imbalance, a type of data bias, in application of a large language model (LLM) to predict patient selection for total knee arthroplasty (TKA). This study utilised data from the Artificial Intelligence to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY) project (ISRCTN18398037). Data included the pre-operative radiology reports of patients referred to secondary care for knee-related complaints from within the North of Scotland. A clinically based LLM (GatorTron) was trained regarding prediction of selection for TKA. Three methods for managing class imbalance were assessed: a standard model, use of class weighting, and majority class undersampling. A total of 7707 individual knee radiology reports were included (dated from 2015 to 2022). The mean text length was 74 words (range 26-275). Only 910/7707 (11.8%) patients underwent TKA surgery (the designated 'minority class'). Class weighting technique performed better for minority class discrimination and calibration compared with the other two techniques (Recall 0.61/AUROC 0.73 for class weighting compared with 0.54/0.70 and 0.59/0.72 for the standard model and majority class undersampling, respectively. There was also significant data loss for majority class undersampling when compared with class-weighting. Use of class-weighting appears to provide the optimal method of training a an LLM to perform analytical tasks on free-text clinical information in the face of significant data bias ('class imbalance'). Such knowledge is an important consideration in the development of high-performance clinical AI models within Trauma and Orthopaedics.

Deep learning (DL) accelerated controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), provides high spatial resolution T1-weighted imaging of the upper abdomen. We aimed to investigate whether DL-CAIPIRINHA-VIBE can improve image quality, vessel conspicuity, and lesion detectability compared to a standard CAIPIRINHA-VIBE in renal imaging at 3 Tesla. In this prospective study, 50 patients with 23 solid and 45 cystic renal lesions underwent MRI with clinical MR sequences, including standard CAIPIRINHA-VIBE and DL-CAIPIRINHA-VIBE sequences in the nephrographic phase at 3 Tesla. Two experienced radiologists independently evaluated both sequences and multiplanar reconstructions (MPR) of the sagittal and coronal planes for image quality with a Likert scale ranging from 1 to 5 (5 =best). Quantitative measurements including the size of the largest lesion and renal lesion contrast ratios were evaluated. DL-CAIPIRINHA-VIBE compared to standard CAIPIRINHA-VIBE showed significantly improved overall image quality, higher scores for renal border delineation, renal sinuses, vessels, adrenal glands, reduced motion artifacts and reduced perceived noise in nephrographic phase images (all p < 0.001). DL-CAIPIRINHA-VIBE with MPR showed superior lesion conspicuity and diagnostic confidence compared to standard CAIPIRINHA-VIBE. However, DL-CAIPIRINHA-VIBE presented a more synthetic appearance and more aliasing artifacts (p < 0.023). The mean size and signal intensity of renal lesions for DL-CAIPIRINHA-VIBE showed no significant differences compared to standard CAIPIRINHA-VIBE (p > 0.9). DL-CAIPIRINHA-VIBE is well suited for kidney imaging in the nephrographic phase, provides good image quality, improved delineation of anatomic structures and renal lesions.

Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cancer (csPCa). Developing AI-based systems to identify csPCa using bpMRI can transform prostate cancer (PCa) management by improving efficiency and cost-effectiveness. However, current state-of-the-art methods using convolutional neural networks (CNNs) and Transformers are limited in learning in-plane and three-dimensional spatial information from anisotropic bpMRI. Their performances also depend on the availability of large, diverse, and well-annotated bpMRI datasets. To address these challenges, we propose the Zonal-aware Self-supervised Mesh Network (Z-SSMNet), which adaptively integrates multi-dimensional (2D/2.5D/3D) convolutions to learn dense intra-slice information and sparse inter-slice information of the anisotropic bpMRI in a balanced manner. We also propose a self-supervised learning (SSL) technique that effectively captures both intra-slice and inter-slice semantic information using large-scale unlabeled data. Furthermore, we constrain the network to focus on the zonal anatomical regions to improve the detection and diagnosis capability of csPCa. We conducted extensive experiments on the PI-CAI (Prostate Imaging - Cancer AI) dataset comprising 10000+ multi-center and multi-scanner data. Our Z-SSMNet excelled in both lesion-level detection (AP score of 0.633) and patient-level diagnosis (AUROC score of 0.881), securing the top position in the Open Development Phase of the PI-CAI challenge and maintained strong performance, achieving an AP score of 0.690 and an AUROC score of 0.909, and securing the second-place ranking in the Closed Testing Phase. These findings underscore the potential of AI-driven systems for csPCa diagnosis and management.

Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive capabilities of preoperative blood like albumin-bilirubin (ALBI), controlling nutritional status (CONUT), prognostic nutritional index (PNI) and CT-imaging nutritional indicators like skeletal muscle index (SMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), visceral to subcutaneous adipose tissue ratio (VSR) in iCCA patients undergoing curative hepatectomy. 290 iCCA patients from two centers were studied. Preoperative blood and CT-imaging nutritional indicators were evaluated. Short-term outcomes like complications, early recurrence (ER) and very early recurrence (VER), and overall survival (OS) as long-term outcome were assessed. Six machine learning (ML) models, including Gradient Boosting (GB) survival analysis, were developed to predict OS. Preoperative blood nutritional indicators significantly associated with postoperative complications. CT-imaging nutritional indicators show insignificant associations with short-term outcomes. All preoperative nutritional indicators were not effective in predicting early tumor recurrence. For long-term outcomes, ALBI, CONUT, PNI, SMI, and VSR were significantly associated with OS. Six ML survival models demonstrated strong and stable performance. GB model showed the best predictive performance (C-index: 0.755 in training cohorts, 0.714 in validation cohorts). Time-dependent ROC, calibration, and decision curve analysis confirmed its clinical value. Preoperative ALBI, CONUT, and PNI scores significantly correlated with complications but not ER. Four Image Nutritional Indicators were ineffective in evaluating short-term outcomes. Six ML models were developed based on nutritional and clinicopathological variables to predict iCCA prognosis.