Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database

Background and objective: The usage of machine learning in medical diagnosis and treatment has witnessed significant growth in recent years through the development of computer-aided diagnosis systems that are often relying on annotated medical radiology images. However, the availability of large annotated image datasets remains a major obstacle since the process of annotation is time-consuming and costly. This paper explores how to automatically annotate a database of medical radiology images with regard to their semantic similarity. Material and methods: An automated, unsupervised approach is used to construct a large annotated dataset of medical radiology images originating from Clinical Hospital Centre Rijeka, Croatia, utilising multimodal sources, including images, DICOM metadata, and narrative diagnoses. Several appropriate feature extractors are tested for each of the data sources, and their utility is evaluated using k-means and k-medoids clustering on a representative data subset. Results: The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. Conclusion: The results suggest that fusing the embeddings of all three data sources together works best for the task of unsupervised clustering of large-scale medical data, resulting in the most concise clusters. Hence, this work is the first step towards building a much larger and more fine-grained annotated dataset of medical radiology images

The four-minute approach revisited : accelerating MRI-based multi-factorial age estimation

Objectives: This feasibility study aimed to investigate the reliability of multi-factorial age estimation based on MR data of the hand, wisdom teeth and the clavicles with reduced acquisition time. Methods: The raw MR data of 34 volunteers-acquired on a 3T system and using acquisition times (TA) of 3:46 min (hand), 5:29 min (clavicles) and 10:46 min (teeth)-were retrospectively undersampled applying the commercially available CAIPIRINHA technique. Automatic and radiological age estimation methods were applied to the original image data as well as undersampled data to investigate the reliability of age estimates with decreasing acquisition time. Reliability was investigated determining standard deviation (SSD) and mean (MSD) of signed differences, intra-class correlation (ICC) and by performing Bland-Altman analysis. Results: Automatic age estimation generally showed very high reliability (SSD < 0.90 years) even for very short acquisition times (SSD ≈ 0.20 years for a total TA of 4 min). Radiological age estimation provided highly reliable results for images of the hand (ICC ≥ 0.96) and the teeth (ICC ≥ 0.79) for short acquisition times (TA = 16 s for the hand, TA = 2:21 min for the teeth), imaging data of the clavicles allowed for moderate acceleration (TA = 1:25 min, ICC ≥ 0.71). Conclusions: The results demonstrate that reliable multi-factorial age estimation based on MRI of the hand, wisdom teeth and the clavicles can be performed using images acquired with a total acquisition time of 4 min

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Pediatric radius torus fractures in x-rays—how computer vision could render lateral projections obsolete

It is an indisputable dogma in extremity radiography to acquire x-ray studies in at least two complementary projections, which is also true for distal radius fractures in children. However, there is cautious hope that computer vision could enable breaking with this tradition in minor injuries, clinically lacking malalignment. We trained three different state-of-the-art convolutional neural networks (CNNs) on a dataset of 2,474 images: 1,237 images were posteroanterior (PA) pediatric wrist radiographs containing isolated distal radius torus fractures, and 1,237 images were normal controls without fractures. The task was to classify images into fractured and non-fractured. In total, 200 previously unseen images (100 per class) served as test set. CNN predictions reached area under the curves (AUCs) up to 98% [95% confidence interval (CI) 96.6%–99.5%], consistently exceeding human expert ratings (mean AUC 93.5%, 95% CI 89.9%–97.2%). Following training on larger data sets CNNs might be able to effectively rule out the presence of a distal radius fracture, enabling to consider foregoing the yet inevitable lateral projection in children. Built into the radiography workflow, such an algorithm could contribute to radiation hygiene and patient comfort

The “cardiac neglect”: a gentle reminder to radiologists interpreting contrast-enhanced abdominal MDCT

Myocardial infarction (MI) may be visible on contrast-enhanced multidetector computed tomography (MDCT) scans of the abdomen. In the previous literature, potentially missed MI in abdominal MDCTs was not perceived as an issue in radiology. This retrospective single-center study assessed the frequency of detectable myocardial hypoperfusion in contrast-enhanced abdominal MDCTs. We identified 107 patients between 2006 and 2022 who had abdominal MDCTs on the same day or the day before a catheter-proven or clinically evident diagnosis of MI. After reviewing the digital patient records and applying the exclusion criteria, we included 38 patients, with 19 showing areas of myocardial hypoperfusion. All MDCT studies were non ECG-gated. The delay between the MDCT examination and MI diagnosis was shorter in studies with myocardial hypoperfusion (7.4±6.5 hours and 13.8±12.5 hours) but not statistically significant p=0.054. Only 2 of 19 (11%) of these pathologies had been noted in the written radiology reports. The most common cardinal symptom was epigastric pain (50%), followed by polytrauma (21%). STEMI was significantly more common in cases of myocardial hypoperfusion p=0.009. Overall, 16 of 38 (42%) patients died because of acute MI. Based on extrapolations using local MDCT rates, we estimate several thousand radiologically missed MI cases worldwide per year

Ultra-low-dose lung multidetector computed tomography in children - Approaching 0.2 millisievert

PURPOSE To compare objective and subjective parameters in image quality and radiation dose of two MDCTs (helical 64 detector CT vs. axial 256 detector CT) in paediatric lung CT. METHODS Radiation dose and image quality were compared between non-enhanced lung CT from a helical 64-slice multidetector CT (MDCT 1) and a 256-slice scanner (MDCT 2) with axial wide-cone acquisition and using deep learning image reconstruction. In 23 size-matched paediatric studies (age 2-18 years) from each scanner, the radiation exposure, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image sharpness and delineation of small airways were assessed. Subjective image quality was rated by 6 paediatric radiologists. RESULTS While MDCT 2 provided higher SNR and CNR, subjective image quality was not significantly different between studies from both scanners. Radiation exposure was lower in studies from MDCT 2 (CTDIvol 0.26 ± 0.14 mGy, effective dose 0.23 ± 0.11 mSv) than from MDCT 1 (CTDIvol 0.96 ± 0.52 mGy, effective dose 1.13 ± 0.58 mSv), p < 0.001. Despite lower radiation dose for the scout images, the relative scout-scan-ratio increased from 2.64 ± 1.42 % in MDCT 1 to 6.60 ± 5.03 % in MDCT 2 (p = 0.001). CONCLUSIONS By using latest scanner technology effective radiation dose can be reduced to 0.1-0.3 mSv for lung CT in children without compromising image quality. Scout image dose increasingly accounts for substantial portions of the total scan dose and needs to be optimized. In children CT should be performed on state-of-the-art MDCT scanners with size-adapted exposure protocols and iterative reconstruction

Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach

Wrist fractures are commonly diagnosed using X-ray imaging, supplemented by magnetic resonance imaging and computed tomography when required. Radiologists can sometimes overlook the fractures because they are difficult to spot. In contrast, some fractures can be easily spotted and only slow down the radiologists because of the reporting systems. We propose a machine learning model based on the YOLOv4 method that can help solve these issues. The rigorous testing on three levels showed that the YOLOv4-based model obtained significantly better results in comparison to the state-of-the-art method based on the U-Net model. In the comparison against five radiologists, YOLO 512 Anchor model-AI (the best performing YOLOv4-based model) was significantly better than the four radiologists (AI AUC-ROC =0.965, Radiologist average AUC-ROC =0.831&plusmn;0.075). Furthermore, we have shown that three out of five radiologists significantly improved their performance when aided by the AI model. Finally, we compared our work with other related work and discussed what to consider when building an ML-based predictive model for wrist fracture detection. All our findings are based on a complex dataset of 19,700 pediatric X-ray images

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones&#8212;common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to 91.16 % and 86.22 % , respectively)