Aims: Prognosis of lung mathology severity after Covid-19 infection using chest X-ray time series Background: We have been inspired by methods analysing time series of images in remote sensing for change detection. During the current Covid-19 pandemic, our motivation is to provide an automatic tool to predict severity of lung pathologies due to Covid-19. This can be done by analysing images of the same patient acquired at different dates. Since no analytical model is available, and also no accurate quantification tools can be used due to many unknowns about the pathology, feature-free methods are good candidates to analyse such temporal images. Objective: This contribution helps improving performances of medical structures facing the Covid-19 pandemic. The first impact is medical and social since more lives could be saved with a 92% rate of good prognosis. In addition to that, patients in intensive care units (up to 15%) could a posteriori suffer from less sequels due to an early and accurate prognosis of their PP. Moreover, accurate prognosis can lead to a better planning of patient’s transfer between units and hospitals, which is linked to the second claimed economical impact. Indeed, prognosis is linked to lower treatment costs due to an optimized predictive protocol using ragiological prognosis. Methods: Using Convolutional Neural Networks (CNN) in combination with Recurrent Neural Networks (RNN). Spatial and temporal features are combines to analyse image time series. A prognosis score is delivered indicating the severity of the pathology. Learning is made on a publicly available database. Results: When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%. Sensitivity and specificity rates are also very interesting. Conclusion: Our method is segmentation-free, which makes it competitive with respect to other assessment methods relying on time-consuming lung segmentation algorithms. When applied on radiographic data, the proposed ProgNet architecture showed promising results with good classification performances, especially for ambiguous cases. Specifically, the reported low false positive rates are interesting for an accurate and personalised care workflow.
Objective: This study aims to investigate the use of 3D printing techniques for the fabrication of physical breast phantoms, suitable for conventional and phase contrast breast imaging. Such phantoms could provide essential information for the design, development and optimization of emerging X-ray imaging modalities. Materials and Methods: Physical phantoms were constructed using two 3D printing techniques: Fused Deposition Modeling and Stereolithography. Eight materials suitable for 3D printing, including thermoplastic filaments and photopolymer resins, were investigated for the optimal representation of breast tissues, based on their attenuation and refractive characteristics. The phantoms consisted of a 3D-printed mold, which was then manually filled with paraffin wax. Additionally, a 3D complex-patterned layer and details representing abnormalities were embedded in different depths. Images of the phantoms were obtained in attenuation and phase contrast mode. Experiments were conducted using an X-ray microfocus tube with Tungsten anode set to 55kVp, combined with a photon-counting detector. The distance between source and detector was 56.5cm. The images were acquired at different object-to-detector distances starting from 5cm up to 40cm in a free space propagation set-up. Results and Conclusion: Results show that among all combinations with paraffin used as an adipose substitute, phantoms created with the Stereolithography technique and resins (especially Flex) as glandular equivalent, were found to be more appropriate for both attenuation and phase contrast imaging. The edge enhancement effect was well observed in the experimental images acquired at 35cm object-to-detector distance, indicating the potential for improved feature visualization using this set-up in phase contrast compared to attenuation mode.
Aims: Digital retinal images are commonly used for hard exudates and lesion detection. These images are rarely noiseless and therefore before any further processing they should be underwent noise removal. Background: An efficient segmentation method is then needed to detect and discern the lesions from the retinal area. Objective: In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The aim of this study is to present a supervised/semi-supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Methods: Ripplet transform and cycle spinning method is first used to remove the noises and artifacts. Results: The noises may be normal or any other commonly occurring forms such as salt and pepper. The image is transformed into fuzzy domain after it is denoised. Conclusion: A cellular learning automata model is used to detect any abnormality on the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to improve the adaptability and efficiency of the cellular automata.Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method.
Background:Ultrasound has been widely used in clinical settings for the assessment of different types of Arthritis as well as in their management. This Review study assessed the diagnostic value of Ultrasonography in comparison with major types of Arthritis namely Osteoarthritis (OA), Rheumatoid Arthritis (RA), Gouty Arthritis (GA), Pseudogout (Calcium Pyrophosphate Deposition Disease - CPPD), Psoriatic Arthritis (PA), Infectious Arthritis (IA) and Spondyloarthritis (SA).Methodology:Computerized literature search of PubMed was conducted from 1990 to present, for publications in English on diagnostic ultrasonography and major types of arthritis. A total of 206 publications were identified. Experimental and clinical studies that focused on the ultrasound features of the major types of Arthritis were accepted. A total of 52 out of the 206 publications, met our search criteria. Among these, 12 studies focused on OA, 7 on RA, 7 on GA, 5 on CPPD, 10 on PA, 4 on IA, and 7 on SA. From all the studies, some distinctive US features are reviewed for each of the major arthritis. Some of the features were unique and some overlapped.Conclusion:Ultrasound may demonstrate the ability to differentiate between the major types of Arthritis on a basic level when combined with history and physical examination. This can prove to be beneficial in the early diagnosis of the major types of arthritis, but with few limitations. This review literature shows that Ultrasound can be very helpful in bed side analysis of the major types of arthritis as well as in differentiating between them, because this modality besides being non-invasive is also very cheap.
Background: The anatomical assessment of the arrangement of facial soft tissues has important applications in different fields from orthodontics to plastic surgery. One of the issues concerns the relationship between facial soft tissue thickness and skeletal class. Literature mainly deals with adult populations, whereas very few studies have been focused on children. Objective: This study aims at investigating the relationship between midline facial soft tissue thickness and skeletal classes in Italian pre-treatment orthodontic child patients. Methods: Lateral cephalometric X-ray films were obtained from 220 healthy Caucasoid children (91 males and 129 females), aged between 6 and 18 years (Class I: 41 males and 70 females; Class II: 18 males and 25 females; Class III: 32 males and 34 females). All the films were digitized and 14 soft tissue thicknesses were measured on the midface; in addition, the skeletal class was assessed according to the corrected ANB angle (ANBc). Differences in facial soft tissue thickness according to sex and skeletal class were assessed through two-way ANOVA test (p<0.01). Results: Statistically significant differences according to sex were found for labrale superius, stomion and labrale inferius, with thicker soft tissues in males than in females (p<0.01). Only measurements at labrale superius and gnathion showed statistically significant differences according to skeletal class, with thicker soft tissues in Class III children and thinner ones in Class II children (p<0.01). Conclusion: The limited number of investigations, as well as the differences in protocols, renders the comparison of results from different studies difficult, suggesting further investigations to enlighten this complex and debated anatomical issue.
