Cephalometric Landmark Detection (CLD), i.e. annotating interest points in lateral X-ray images, is the crucial first step of every orthodontic therapy. While CLD has immense potential for automation using Deep Learning methods, carefully crafted contemporary approaches using convolutional neural networks and heatmap prediction do not qualify for large-scale clinical application due to insufficient performance. We propose a novel approach using Vision Transformers (ViTs) with direct coordinate prediction, avoiding the memory-intensive heatmap prediction common in previous work. Through extensive ablation studies comparing our method against contemporary CNN architectures (ConvNext V2) and heatmap-based approaches (Segformer), we demonstrate that ViTs with coordinate prediction achieve superior performance with more than 2 mm improvement in mean radial error compared to state-of-the-art CLD methods. Our results show that while non-adapted CNN architectures perform poorly on the given task, contemporary approaches may be too tailored to specific datasets, failing to generalize to different and especially sparse datasets. We conclude that using general-purpose Vision Transformers with direct coordinate prediction shows great promise for future research on CLD and medical computer vision.

BackgroundThe precise pneumoconiosis staging suffers from progressive pair label noise (PPLN) in chest X-ray datasets, because adjacent stages are confused due to unidentifialble and diffuse opacities in the lung fields. As deep neural networks are employed to aid the disease staging, the performance is degraded under such label noise.ObjectiveThis study improves the effectiveness of pneumoconiosis staging by mitigating the impact of PPLN through network architecture refinement and sample selection mechanism adjustment.MethodsWe propose a novel multi-branch architecture that incorporates the dual-threshold sample selection. Several auxiliary branches are integrated in a two-phase module to learn and predict the <i>progressive feature tendency</i>. A novel difference-based metric is introduced to iteratively obtained the instance-specific thresholds as a complementary criterion of dynamic sample selection. All the samples are finally partitioned into <i>clean</i> and <i>hard</i> sets according to dual-threshold criteria and treated differently by loss functions with penalty terms.ResultsCompared with the state-of-the-art, the proposed method obtains the best metrics (accuracy: 90.92%, precision: 84.25%, sensitivity: 81.11%, F1-score: 82.06%, and AUC: 94.64%) under real-world PPLN, and is less sensitive to the rise of synthetic PPLN rate. An ablation study validates the respective contributions of critical modules and demonstrates how variations of essential hyperparameters affect model performance.ConclusionsThe proposed method achieves substantial effectiveness and robustness against PPLN in pneumoconiosis dataset, and can further assist physicians in diagnosing the disease with a higher accuracy and confidence.

To assess the added value of artificial intelligence (AI) for radiologists and emergency physicians in the radiographic detection of pelvic fractures. In this retrospective study, one junior radiologist reviewed 940 X-rays of patients admitted to emergency for a fall with suspicion of pelvic fracture between March 2020 and June 2021. The radiologist analyzed the X-rays alone and then using an AI system (BoneView). In a random sample of 100 exams, the same procedure was repeated alongside five other readers (three radiologists and two emergency physicians with 3-30 years of experience). The reference diagnosis was based on the patient's full set of medical imaging exams and medical records in the months following emergency admission. A total of 633 confirmed pelvic fractures (64.8% from hip and 35.2% from pelvic ring) in 940 patients and 68 pelvic fractures (60% from hip and 40% from pelvic ring) in the 100-patient sample were included. In the whole dataset, the junior radiologist achieved a significant sensitivity improvement with AI assistance (Se_-PELVIC = 77.25% to 83.73%; p < 0.001, Se_-HIP 93.24 to 96.49%; p < 0.001 and Se_{-PELVIC RING} 54.60% to 64.50%; p < 0.001). However, there was a significant decrease in specificity with AI assistance (Spe_-PELVIC = 95.24% to 93.25%; p = 0.005 and Spe_-HIP = 98.30% to 96.90%; p = 0.005). In the 100-patient sample, the two emergency physicians obtained an improvement in fracture detection sensitivity across the pelvic area + 14.70% (p = 0.0011) and + 10.29% (p < 0.007) respectively without a significant decrease in specificity. For hip fractures, E1's sensitivity increased from 59.46% to 70.27% (p = 0.04), and E2's sensitivity increased from 78.38% to 86.49% (p = 0.08). For pelvic ring fractures, E1's sensitivity increased from 12.90% to 32.26% (p = 0.012), and E2's sensitivity increased from 19.35% to 32.26% (p = 0.043). AI improved the diagnostic performance for emergency physicians and radiologists with limited experience in pelvic fracture screening.

The rapid advancement of artificial intelligence (AI) has great ability to impact healthcare. Chest X-rays are essential for diagnosing acute thoracic conditions in the emergency department (ED), but interpretation delays due to radiologist availability can impact clinical decision-making. AI models, including deep learning algorithms, have been explored for diagnostic support, but the potential of large language models (LLMs) in emergency radiology remains largely unexamined. This study assessed ChatGPT's feasibility in interpreting chest X-rays for acute thoracic conditions commonly encountered in the ED. A subset of 1400 images from the NIH Chest X-ray dataset was analyzed, representing seven pathology categories: Atelectasis, Effusion, Emphysema, Pneumothorax, Pneumonia, Mass, and No Finding. ChatGPT 4.0, utilizing the "X-Ray Interpreter" add-on, was evaluated for its diagnostic performance across these categories. ChatGPT demonstrated high performance in identifying normal chest X-rays, with a sensitivity of 98.9 %, specificity of 93.9 %, and accuracy of 94.7 %. However, the model's performance varied across pathologies. The best results were observed in diagnosing pneumonia (sensitivity 76.2 %, specificity 93.7 %) and pneumothorax (sensitivity 77.4 %, specificity 89.1 %), while performance for atelectasis and emphysema was lower. ChatGPT demonstrates potential as a supplementary tool for differentiating normal from abnormal chest X-rays, with promising results for certain pathologies like pneumonia. However, its diagnostic accuracy for more subtle conditions requires improvement. Further research integrating ChatGPT with specialized image recognition models could enhance its performance, offering new possibilities in medical imaging and education.

Large-scale generative models have garnered significant attention in the field of medical imaging, particularly for image editing utilizing diffusion models. However, current research has predominantly concentrated on pathological editing involving single or a limited number of labels, making it challenging to achieve precise modifications. Inaccurate alterations may lead to substantial discrepancies between the generated and original images, thereby impacting the clinical applicability of these models. This paper presents a diffusion model with untangling capabilities applied to chest X-ray image editing, incorporating a mask-based mechanism for bone and organ information. We successfully perform multi-label pathological editing of chest X-ray images without compromising the integrity of the original thoracic structure. The proposed technology comprises a chest X-ray image classifier and an intricate organ mask; the classifier supplies essential feature labels that require untangling for the stabilized diffusion model, while the complex organ mask facilitates directed and controllable edits to chest X-rays. We assessed the outcomes of our proposed algorithm, named Chest X-rays_Mpe, using MS-SSIM and CLIP scores alongside qualitative evaluations conducted by radiology experts. The results indicate that our approach surpasses existing algorithms across both quantitative and qualitative metrics.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.

The intertumoral and intratumoral heterogeneity of colorectal adenocarcinoma (CRC) at the morphologic level is poorly understood. Previously, we identified morphological patterns associated with CRC molecular subtypes and their distinct molecular motifs. Here we aimed to evaluate the heterogeneity of these patterns across CRC. Three pathologists evaluated dominant, secondary, and tertiary morphology on four sections from four different FFPE blocks per tumor in a pilot set of 22 CRCs. An AI-based image analysis tool was trained on these tumors to evaluate the morphologic heterogeneity on an extended set of 161 stage I-IV primary CRCs (n = 644 H&E sections). We found that most tumors had two or three different dominant morphotypes and the complex tubular (CT) morphotype was the most common. The CT morphotype showed no combinatorial preferences. Desmoplastic (DE) morphotype was rarely dominant and rarely combined with other dominant morphotypes. Mucinous (MU) morphotype was mostly combined with solid/trabecular (TB) and papillary (PP) morphotypes. Most tumors showed medium or high heterogeneity, but no associations were found between heterogeneity and clinical parameters. A higher proportion of DE morphotype was associated with higher T-stage, N-stage, distant metastases, AJCC stage, and shorter overall survival (OS) and relapse-free survival (RFS). A higher proportion of MU morphotype was associated with higher grade, right side, and microsatellite instability (MSI). PP morphotype was associated with earlier T- and N-stage, absence of metastases, and improved OS and RFS. CT was linked to left side, lower grade, and better survival in stage I-III patients. MSI tumors showed higher proportions of MU and TB, and lower CT and PP morphotypes. These findings suggest that morphological shifts accompany tumor progression and highlight the need for extensive sampling and AI-based analysis. In conclusion, we observed unexpectedly high intratumoral morphological heterogeneity of CRC and found that it is not heterogeneity per se, but the proportions of morphologies that are associated with clinical outcomes.

Sparse-View X-ray 3D Foot Reconstruction aims to reconstruct the three-dimensional structure of the foot from sparse-view X-ray images, a challenging task due to data sparsity and limited viewpoints. This paper presents a novel method using a multi-scale geometric Transformer to enhance reconstruction accuracy and detail representation. Geometric position encoding technology and a window mechanism are introduced to divide X-ray images into local areas, finely capturing local features. A multi-scale Transformer module based on Neural Radiance Fields (NeRF) enhances the model's ability to express and capture details in complex structures. An adaptive weight learning strategy further optimizes the Transformer's feature extraction and long-range dependency modelling. Experimental results demonstrate that the proposed method significantly improves the reconstruction accuracy and detail preservation of the foot structure under sparse-view X-ray conditions. The multi-scale geometric Transformer effectively captures local and global features, leading to more accurate and detailed 3D reconstructions. The proposed method advances medical image reconstruction, significantly improving the accuracy and detail preservation of 3D foot reconstructions from sparse-view X-ray images.

In pancreatic stereotactic body radiotherapy (SBRT), accurate motion management is crucial for the safe delivery of high doses per fraction. Intra-fraction tracking with magnetic resonance imaging-guidance for gated SBRT has shown potential for improved local control. Visualisation of pancreas (and surrounding organs) remains challenging in intra-fraction kilo-voltage (kV) imaging, requiring implanted fiducials. In this study, we investigate patient-specific deep-learning approaches to track the gross-tumour-volume (GTV), pancreas-head and the whole-pancreas in intra-fraction kV images. Conditional-generative-adversarial-networks were trained and tested on data from 25 patients enrolled in an ethics-approved pancreatic SBRT trial for contour prediction on intra-fraction 2D kV images. Labelled digitally-reconstructed-radiographs (DRRs) were generated from contoured planning-computed-tomography (CTs) (CT-DRRs) and cone-beam-CTs (CBCT-DRRs). A population model was trained using CT-DRRs of 19 patients. Two patient-specific model types were created for six additional patients by fine-tuning the population model using CBCT-DRRs (CBCT-models) or CT-DRRs (CT-models) acquired in exhale-breath-hold. Model predictions on unseen triggered-kV images from the corresponding six patients were evaluated against projected-contours using Dice-Similarity-Coefficient (DSC), centroid-error (CE), average Hausdorff-distance (AHD), and Hausdorff-distance at 95th-percentile (HD95). The mean ± 1SD (standard-deviation) DSCs were 0.86 ± 0.09 (CBCT-models) and 0.78 ± 0.12 (CT-models). For AHD and CE, the CBCT-model predicted contours within 2.0 mm ≥90.3 % of the time, while HD95 was within 5.0 mm ≥90.0 % of the time, and had a prediction time of 29.2 ± 3.7 ms per contour. The patient-specific CBCT-models outperformed the CT-models and predicted the three contours with 90th-percentile error ≤2.0 mm, indicating the potential for clinical real-time application.