This study aimed to evaluate the image quality and clinical utility of a deep learning reconstruction (DLR) algorithm in ultra-high-resolution computed tomography (UHR-CT) for the diagnosis of pancreatic cystic neoplasms (PCNs). This retrospective study included 45 patients with PCNs between March 2020 and February 2022. Contrast-enhanced UHR-CT images were obtained and reconstructed using DLR and hybrid iterative reconstruction (IR). Image noise and contrast-to-noise ratio (CNR) were measured. Two radiologists assessed the diagnostic performance of the imaging findings associated with PCNs using a 5-point Likert scale. The diagnostic performance metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC), were calculated. Quantitative and qualitative features were compared between CT with DLR and hybrid IR. Interobserver agreement for qualitative assessments was also analyzed. DLR significantly reduced image noise and increased CNR compared to hybrid IR for all objects (p < 0.001). Radiologists rated DLR images as superior in overall quality, lesion delineation, and vessel conspicuity (p < 0.001). DLR produced higher AUROC values for diagnostic imaging findings (ductal communication: 0.887‒0.938 vs. 0.816‒0.827 and enhanced mural nodule: 0.843‒0.916 vs. 0.785‒0.801), although DLR did not directly improve sensitivity, specificity, and accuracy. Interobserver agreement for qualitative assessments was higher in CT with DLR (κ = 0.69‒0.82 vs. 0.57‒0.73). DLR improved image quality and diagnostic performance by effectively reducing image noise and improving lesion conspicuity in the diagnosis of PCNs on UHR-CT. The DLR demonstrated greater diagnostic confidence for the assessment of imaging findings associated with PCNs.

To train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate cancer and to compare this against non-MRI models. 2895 MRIs were collected from two sources (1630 internal, 1265 public) in this retrospective study. Risk models compared were: Prostate Cancer Prevention Trial Risk Calculator 2.0, Prostate Biopsy Collaborative Group Calculator, PSA density, U-Net segmentation, and U-Net combined with clinical parameters. The reference standard was histopathology or negative follow-up. Performance metrics were calculated by simulating a triaging workflow compared to radiologist interpreting all exams on a test set of 465 patients. Sensitivity and specificity differences were assessed using the McNemar test. Differences in PPV and NPV were assessed using the Leisenring, Alonzo and Pepe generalized score statistic. Equivalence test p-values were adjusted within each measure using Benjamini-Hochberg correction. Triaging using U-Net with clinical parameters reduced radiologist workload by 12.5% with sensitivity decrease from 93 to 90% (p = 0.023) and specificity increase from 39 to 47% (p < 0.001). This simulated workload reduction was greater than triaging with risk calculators (3.2% and 1.3%, p < 0.001), and comparable to PSA density (8.4%, p = 0.071) and U-Net alone (11.6%, p = 0.762). Both U-Net triaging strategies increased PPV (+ 2.8% p = 0.005 clinical, + 2.2% p = 0.020 nonclinical), unlike non-U-Net strategies (p > 0.05). NPV remained equivalent for all scenarios (p > 0.05). Clinically-informed U-Net triaging correctly ruled out 20 (13.4%) radiologist false positives (12 PI-RADS = 3, 8 PI-RADS = 4). Of the eight (3.6%) false negatives, two were misclassified by the radiologist. No misclassified case was interpreted as PI-RADS 5. Prostate MRI triaging using machine learning could reduce radiologist workload by 12.5% with a 3% sensitivity decrease and 8% specificity increase, outperforming triaging using non-imaging-based risk models. Further prospective validation is required.

Periventricular white matter injury (PWMI) is the most frequent magnetic resonance imaging (MRI) finding in infants with Cerebral Palsy (CP). We aim to detect CP and identify subtle, sparse PWMI lesions in infants under two years of age with immature brain structures. Based on the characteristic that the responsible lesions are located within five target regions, we first construct a multi-modal dataset including 243 cases with the mask annotations of five target regions for delineating anatomical structures on T1-Weighted Imaging (T1WI) images, masks for lesions on T2-Weighted Imaging (T2WI) images, and categories (CP or Non-CP). Furthermore, we develop a bidirectional projection-based multi-modal fusion transformer (BiP-MFT), incorporating a Bidirectional Projection Fusion Module (BPFM) for integrating the features between five target regions on T1WI images and lesions on T2WI images. Our BiP-MFT achieves subject-level classification accuracy of 0.90, specificity of 0.87, and sensitivity of 0.94. It surpasses the best results of nine comparative methods, with 0.10, 0.08, and 0.09 improvements in classification accuracy, specificity and sensitivity respectively. Our BPFM outperforms eight compared feature fusion strategies using Transformer and U-Net backbones on our dataset. Ablation studies on the dataset annotations and model components justify the effectiveness of our annotation method and the model rationality. The proposed dataset and codes are available at https://github.com/Kai-Qi/BiP-MFT.

Uncertainty estimation methods are essential for the application of artificial intelligence (AI) models in medical image segmentation, particularly in addressing reliability and feasibility challenges in clinical deployment. Despite their significance, the adoption of uncertainty estimation methods in clinical practice remains limited due to the lack of a comprehensive evaluation framework tailored to their clinical usage. To address this gap, a simulation of uncertainty-assisted clinical workflows is conducted, highlighting the roles of uncertainty in model selection, sample screening, and risk visualization. Furthermore, uncertainty evaluation is extended to pixel, sample, and model levels to enable a more thorough assessment. At the pixel level, the Uncertainty Confusion Metric (UCM) is proposed, utilizing density curves to improve robustness against variability in uncertainty distributions and to assess the ability of pixel uncertainty to identify potential errors. At the sample level, the Expected Segmentation Calibration Error (ESCE) is introduced to provide more accurate calibration aligned with Dice, enabling more effective identification of low-quality samples. At the model level, the Harmonic Dice (HDice) metric is developed to integrate uncertainty and accuracy, mitigating the influence of dataset biases and offering a more robust evaluation of model performance on unseen data. Using this systematic evaluation framework, five mainstream uncertainty estimation methods are compared on organ and tumor datasets, providing new insights into their clinical applicability. Extensive experimental analyses validated the practicality and effectiveness of the proposed metrics. This study offers clear guidance for selecting appropriate uncertainty estimation methods in clinical settings, facilitating their integration into clinical workflows and ultimately improving diagnostic efficiency and patient outcomes.

Soft tissue pathologies and bone defects are not easily visible in intra-operative fluoroscopic images; therefore, we develop an end-to-end MRI-to-fluoroscopic image registration framework, aiming to enhance intra-operative visualization for surgeons during orthopedic procedures. The proposed framework utilizes deep learning to segment MRI scans and generate synthetic CT (sCT) volumes. These sCT volumes are then used to produce digitally reconstructed radiographs (DRRs), enabling 2D/3D registration with intra-operative fluoroscopic images. The framework's performance was validated through simulation and cadaver studies for core decompression (CD) surgery, focusing on the registration accuracy of femur and pelvic regions. The framework achieved a mean translational registration accuracy of 2.4 ± 1.0 mm and rotational accuracy of 1.6 ± <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0</mn> <mo>.</mo> <msup><mn>8</mn> <mo>∘</mo></msup> </mrow> </math> for the femoral region in cadaver studies. The method successfully enabled intra-operative visualization of necrotic lesions that were not visible on conventional fluoroscopic images, marking a significant advancement in image guidance for femur and pelvic surgeries. The MRI-to-fluoroscopic registration framework offers a novel approach to image guidance in orthopedic surgeries, exclusively using MRI without the need for CT scans. This approach enhances the visualization of soft tissues and bone defects, reduces radiation exposure, and provides a safer, more effective alternative for intra-operative surgical guidance.

We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized computed tomography angiography gating (CTA). The framework integrates a 3D convolutional neural network (CNN) for US data and an artificial neural network (ANN) for ECG data. A dynamic heart motion phantom, replicating diverse cardiac conditions, including arrhythmias, was used to validate the framework. Performance was assessed across varying QP lengths, cardiac segments, and motions to simulate real-world conditions. The multimodal US-ECG 3D CNN-ANN framework demonstrated improved QP prediction accuracy compared to single-modality ECG-only gating, achieving 96.87% accuracy compared to 85.56%, including scenarios involving arrhythmic conditions. Notably, the framework shows higher accuracy for longer QP durations (100 ms - 200 ms) compared to shorter durations (<100ms), while still outperforming single-modality methods, which often fail to detect shorter quiescent phases, especially in arrhythmic cases. Consistently outperforming single-modality approaches, it achieves reliable QP prediction across cardiac regions, including the whole phantom, interventricular septum, and cardiac wall regions. Analysis of QP prediction accuracy across cardiac segments demonstrated an average accuracy of 92% in clinically relevant echocardiographic views, highlighting the framework's robustness. Combining US and ECG data using a multimodal framework improves QP prediction accuracy under variable cardiac motion, particularly in arrhythmic conditions. Since even small errors in cardiac CTA can result in non-diagnostic scans, the potential benefits of multimodal gating may improve diagnostic scan rates in patients with high and variable heart rates and arrhythmias.

Accurate preoperative detection and analysis of lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC) is essential for the surgical planning and execution of a neck dissection and may directly affect the morbidity and prognosis of patients. Additionally, predicting extranodal extension (ENE) using pre-operative imaging could be particularly valuable in oropharyngeal HPV-positive squamous cell carcinoma, enabling more accurate patient counseling, allowing the decision to favor primary chemoradiotherapy over immediate neck dissection when appropriate. Currently, radiological images are evaluated by radiologists and head and neck oncologists; and automated image interpretation is not part of the current standard of care. Therefore, the value of preoperative image recognition by artificial intelligence (AI) with the large language model (LLM) ChatGPT-4 V was evaluated in this exploratory study based on neck computed tomography (CT) images of HNSCC patients with cervical LNM, and corresponding images without LNM. The objective of this study was to firstly assess the preoperative rater accuracy by comparing clinician assessments of imaging-detected extranodal extension (iENE) and the extent of neck dissection to AI predictions, and secondly to evaluate the pathology-based accuracy by comparing AI predictions to final histopathological outcomes. 45 preoperative CT scans were retrospectively analyzed in this study: 15 cases in which a selective neck dissection (sND) was performed, 15 cases with ensuing radical neck dissection (mrND), and 15 cases without LNM (sND). Of note, image analysis was based on three single images provided to both ChatGPT-4 V and the head and neck surgeons as reviewers. Final pathological characteristics were available in all cases as HNSCC patients had undergone surgery. ChatGPT-4 V was tasked with providing the extent of LNM in the preoperative CT scans and with providing a recommendation for the extent of neck dissection and the detection of iENE. The diagnostic performance of ChatGPT-4 V was reviewed independently by two head and neck surgeons with its accuracy, sensitivity, and specificity being assessed. In this study, ChatGPT-4 V reached a sensitivity of 100% and a specificity of 34.09% in identifying the need for a radical neck dissection based on neck CT images. The sensitivity and specificity of detecting iENE was 100% and 34.15%, respectively. Both human reviewers achieved higher specificity. Notably, ChatGPT-4 V also recommended a mrND and detected iENE on CT images without any cervical LNM. In this exploratory study of 45 preoperative CT Neck scans before a neck dissection, ChatGPT-4 V substantially overestimated the degree and severity of lymph node metastasis in head and neck cancer. While these results suggest that ChatGPT-4 V may not yet be a tool providing added value for surgical planning in head and neck cancer, the unparalleled speed of analysis and well-founded reasoning provided suggests that AI tools may provide added value in the future.

Cone-beam computed tomography (CBCT) imaging plays a crucial role in dentistry, with automatic prediction of anatomical structures on CBCT images potentially enhancing diagnostic and planning procedures. This study aims to predict anatomical structures automatically on CBCT images using a deep learning algorithm. CBCT images from 70 patients were analyzed. Anatomical structures were annotated using a regional segmentation tool within an annotation software by two dentomaxillofacial radiologists. Each volumetric dataset comprised 405 slices, with relevant anatomical structures marked in each slice. Seventy DICOM images were converted to Nifti format, with seven reserved for testing and the remaining sixty-three used for training. The training utilized nnUNetv2 with an initial learning rate of 0.01, decreasing by 0.00001 at each epoch, and was conducted for 1000 epochs. Statistical analysis included accuracy, Dice score, precision, and recall results. The segmentation model achieved an accuracy of 0.99 for nasal fossa, maxillary sinus, nasopalatine canal, mandibular canal, foramen mentale, and foramen mandible, with corresponding Dice scores of 0.85, 0.98, 0.79, 0.73, 0.78, and 0.74, respectively. Precision values ranged from 0.73 to 0.98. Maxillary sinus segmentation exhibited the highest performance, while mandibular canal segmentation showed the lowest performance. The results demonstrate high accuracy and precision across most structures, with varying Dice scores indicating the consistency of segmentation. Overall, our segmentation model exhibits robust performance in delineating anatomical features in CBCT images, promising potential applications in dental diagnostics and treatment planning.

Mammographic breast cancer detection depends on high-quality positioning, which is traditionally assessed and monitored subjectively. This study used artificial intelligence (AI) to evaluate mammography positioning on digital screening mammograms to identify and quantify unmet mammography positioning quality (MPQ). Data were collected within an IRB-approved collaboration. In total, 126 367 digital mammography studies (553 339 images) were processed. Unmet MPQ criteria, including exaggeration, portion cutoff, posterior tissue missing, nipple not in profile, too high on image receptor, inadequate pectoralis length, sagging, and posterior nipple line (PNL) length difference, were evaluated using MPQ AI algorithms. The similarity of unmet MPQ occurrence and rank order was compared for each health system. Altogether, 163 759 and 219 785 unmet MPQ criteria were identified, respectively, at the health systems. The rank order and the probability distribution of the unmet MPQ criteria were not statistically significantly different between health systems (P = .844 and P = .92, respectively). The 3 most-common unmet MPQ criteria were: short PNL length on the craniocaudal (CC) view, inadequate pectoralis muscle, and excessive exaggeration on the CC view. The percentages of unmet positioning criteria out of the total potential unmet positioning criteria at health system 1 and health system 2 were 8.4% (163 759/1 949 922) and 7.3% (219 785/3 030 129), respectively. Artificial intelligence identified a similar distribution of unmet MPQ criteria in 2 health systems' daily work. Knowledge of current commonly unmet MPQ criteria can facilitate the improvement of mammography quality through tailored education strategies.

This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery. A retrospective review was conducted on 461 patients who underwent PLIF for degenerative lumbar diseases. These patients were partitioned into a training set (n=368) and a test set (n=93) in an 8:2 ratio. Clinical models, radiomics models, and DL models were constructed based on logistic regression and random forest, respectively. A combined model was established by integrating these three models. All radiomics and DL features were extracted from sagittal T2-weighted images using 3D slicer software. The least absolute shrinkage and selection operator method selected the optimal radiomics and DL features to build the models. In addition to analyzing the original region of interest (ROI), we also conducted different degrees of mask expansion on the ROI to determine the optimal ROI. The performance of the model was evaluated by using the receiver operating characteristic curve (ROC) and the area under the ROC curve. The differences in AUC were compared by DeLong test. Among the clinical characteristics, patient age, body weight, and preoperative intervertebral distance at the surgical segment are risk factors affecting the fusion outcome. The radiomics model based on MRI with expanded 10 mm mask showed excellent performance (training set AUC=0.814, 95% CI: (0.761-0.866); test set AUC=0.749, 95% CI: [0.631-0.866]). Among all single models, the DL model had the best diagnostic prediction performance, with AUC values of (0.995, 95% CI: [0.991-0.999]) for the training set and (0.803, 95% CI: [0.705-0.902]) for the test set. Compared to all the models, the combined model of clinical features, radiomics features, and DL features had the best diagnostic prediction performance, with AUC values of (0.993, 95% CI: [0.987-0.999]) for the training set and (0.866, 95% CI: [0.778-0.955]) for the test set. The proposed clinical feature-deep learning radiomics model can effectively predict the postoperative efficacy of patients undergoing PLIF surgery and has good clinical applicability.