scholarly journals Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports

2020 ◽  
Vol 28 (5) ◽  
pp. 885-892 ◽  
Author(s):  
Ying Su ◽  
Yi Han ◽  
Jie Liu ◽  
Yue Qiu ◽  
Qian Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Quantitative Analysis ◽  
Disease Severity ◽  
Initial Treatment ◽  
Case Reports ◽  
Ct Images ◽  
Ct Scans ◽  
Serial Ct ◽  
Analysis Of The Images

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.

Quantitative Analysis of the Transcondylar Approach to the Foramen Magnum

Neurosurgery ◽  
2001 ◽  
Vol 49 (4) ◽  
pp. 934-943 ◽  
Author(s):  
John E. Wanebo ◽  
Michael R. Chicoine
Keyword(s):  
Computed Tomography ◽  
Quantitative Analysis ◽  
Ct Images ◽  
Foramen Magnum ◽  
Ct Scans ◽  
Computed Tomographic ◽  
Suboccipital Craniotomy ◽  
Before And After ◽  
Skull Measurements ◽  
C 50

Abstract OBJECTIVE Condylar resection with suboccipital craniotomy increases foramen magnum exposure, but guidelines for when this is necessary are not defined. Cadaveric and computed tomography evaluations were completed to guide decision-making regarding the use and extent of condylar resection. METHODS Quantitative analysis of foramen magnum surgical exposures was performed on 32 skulls (64 sides) and 6 cadaveric dissections (12 sides). Computed tomographic (CT) scans were performed on cadaveric heads before and after condylar resections. Digitized images of dry skulls and CT images of cadaver heads were quantitatively analyzed. Predissection CT measurements of cadaveric heads guided extent of condylar resections, and resection accuracy was assessed with postdissection CT scans. RESULTS Skull measurements (means in parentheses) included the foramen magnum area (7.8 cm2), length (3.6 cm), width (3.1 cm), anteroposterior condylar length (2.3 cm), and axial condylar length (2.5 cm). Mean widths of potential surgical exposures for skulls were obtained for A) suboccipital craniotomy (2.3 cm), B) with 25% (2.6 cm), and C) 50% condylar resection (3.0 cm). Mean angles of exposure were as follows: A, 38.4 degrees; B, 49.1 degrees; and C, 54.3 degrees. CT scans of cadaveric heads before and after dissections yielded measurements of exposure equivalent to measurements found on the dry skulls. CONCLUSION On average, lateral exposure increases by 3 mm (13%) and 7 mm (30%) for 25 and 50% condylar resection, respectively, compared with suboccipital craniotomy alone. Angles of exposure increase by 10.7 degrees (28%) and 15.9 degrees (41%). Measurements of CT images can be used preoperatively to help analyze the need for condylar resection and intraoperatively to guide the extent of condylar resection.

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations

2021 ◽  
pp. 028418512110449
Author(s):  
Yoshiharu Ohno ◽  
Kota Aoyagi ◽  
Daisuke Takenaka ◽  
Takeshi Yoshikawa ◽  
Yasuko Fujisawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Disease Severity ◽  
Thin Section ◽  
Ct Scans ◽  
Normal Lung ◽  
Quantitative Index ◽  
Serial Ct ◽  
Ct Texture Analysis

Background The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). Purpose To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. Material and Methods A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as “Stable” (n = 188), “Worse” (n = 98) and “Improved” (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. Results Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses ( P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). Conclusion ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

scholarly journals A Method for Tooth Model Reconstruction Based on Integration of Multimodal Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xinwen Zhou ◽  
Yangzhou Gan ◽  
Jing Xiong ◽  
Dongxia Zhang ◽  
Qunfei Zhao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Orthodontic Treatment ◽  
Ct Images ◽  
Ct Scans ◽  
Reconstruction Method ◽  
Model Reconstruction ◽  
Crown Area ◽  
Computer Aided ◽  
Multimodal Images ◽  
Reconstructed Model

A complete digital tooth model is needed for computer-aided orthodontic treatment. However, current methods mainly use computed tomography (CT) images to reconstruct the tooth model which may require multiple CT scans during orthodontic progress, and the reconstructed model is also inaccurate in crown area. This study developed a tooth model reconstruction method based on integration of CT images and laser scan images to overcome these disadvantages. In the method, crown models and complete tooth models are first reconstructed, respectively, from laser scan images and CT images. Then, crown models from laser scan images and tooth models from CT images are registered. Finally, the crown from laser scan images and root from CT images were fused to obtain a new tooth model. Experimental results verified that the developed method is effective to generate the complete tooth model by integrating CT images and laser scan images. Using the proposed method, the reconstructed models provide more accurate crown than CT images, and it is feasible to obtain complete tooth models at any stage of orthodontic treatment by using one CT scan at the pretreatment stage and one laser scan at that stage to avoid multiple CT scans.

Early ischemic lesion on computed tomography: predictor of poor outcome among survivors of aneurysmal subarachnoid hemorrhage

2007 ◽  
Vol 107 (6) ◽  
pp. 1074-1079 ◽  
Author(s):  
Jari Siironen ◽  
Matti Porras ◽  
Joona Varis ◽  
Kristiina Poussa ◽  
Juha Hernesniemi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Subarachnoid Hemorrhage ◽  
Independent Predictor ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Ct Scans ◽  
Ischemic Lesion ◽  
Poor Outcome ◽  
Serial Ct ◽  
Early Lesion

Object Identifying ischemic lesions after subarachnoid hemorrhage (SAH) is important because the appearance of these lesions on follow-up imaging correlates with a poor outcome. The effect of ischemic lesions seen on computed tomography (CT) scans during the first days of treatment remains unknown, however. Methods In 156 patients with SAH, clinical course and outcome, as well as the appearance of ischemic lesions on serial CT scans, were prospectively monitored for 3 months. At 3 months after SAH, magnetic resonance imaging was performed to detect permanent lesions that had not been visible on CT. Results Of the 53 patients with no lesions on any of the follow-up CT scans, four (8%) had a poor outcome. Of the 52 patients with a new hypodense lesion on the first postoperative day CT, 23 (44%) had a poor outcome. Among the remaining 51 patients with a lesion appearing later than the first postoperative morning, 10 (20%) had a poor outcome (p < 0.001). After adjusting for patient age; clinical condition on admission; amounts of subarachnoid, intracerebral, and intraventricular blood; and plasma glucose and D-dimer levels, a hypodense lesion on CT on the first postoperative morning was an independent predictor of poor outcome after SAH (odds ratio 7.27, 95% confidence interval 1.54–34.37, p < 0.05). Conclusions A new hypodense lesion on early postoperative CT seems to be an independent risk factor for poor outcome after SAH, and this early lesion development may be more detrimental to clinical outcome than a later lesion occurrence.

Turbinate atrophy evaluation in pigs by computed tomography

2003 ◽  
Vol 51 (4) ◽  
pp. 485-491 ◽  
Author(s):  
T. Magyar ◽  
F. Kovács ◽  
T. Donkó ◽  
H. Bíró ◽  
R. Romvári ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Nasal Cavity ◽  
Scoring System ◽  
Ct Images ◽  
Area Ratio ◽  
Ct Scans ◽  
Non Invasive ◽  
Visual Scoring ◽  
Ratio Determination ◽  
Visualisation Technique

Computed tomography (CT), a non-invasive visualisation technique was applied for imaging the bony structures of the nasal cavity of pigs, and compared to the traditional scoring system of turbinate atrophy in swine. Twenty-three 27-week-old pigs representing various stages of turbinate atrophy were used. Nasal structures were visually scored on CT scans and transversal cuts of the noses at the level of the first upper premolar teeth using the same scoring system in both cases. A tissue/air area ratio was also determined based on density differences. A highly significant correlation was found between visual scoring of CT images and transversal cuts of pig noses (r = 0.98, p < 0.0001) as well as between visual scoring of CT images and tissue/air area ratio determination (r = -0.82, p < 0.0001).

scholarly journals Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei-Lei Wu ◽  
Jin-Long Wang ◽  
Wei Huang ◽  
Xuan Liu ◽  
Yang-Yu Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Squamous Cell Carcinoma ◽  
Quantitative Analysis ◽  
High Risk ◽  
Clinical Features ◽  
Risk Groups ◽  
Ct Images ◽  
Prognostic Modeling ◽  
Model C ◽  
Ct Features

ObjectiveTo evaluate the effectiveness of a novel computerized quantitative analysis based on histopathological and computed tomography (CT) images for predicting the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients.MethodsWe retrospectively reviewed the medical records of 153 ESCC patients who underwent esophagectomy alone and quantitatively analyzed digital histological specimens and diagnostic CT images. We cut pathological images (6000 × 6000) into 50 × 50 patches; each patient had 14,400 patches. Cluster analysis was used to process these patches. We used the pathological clusters to all patches ratio (PCPR) of each case for pathological features and we obtained 20 PCPR quantitative features. Totally, 125 computerized quantitative (20 PCPR and 105 CT) features were extracted. We used a recursive feature elimination approach to select features. A Cox hazard model with L1 penalization was used for prognostic indexing. We compared the following prognostic models: Model A: clinical features; Model B: quantitative CT and clinical features; Model C: quantitative histopathological and clinical features; and Model D: combined information of clinical, CT, and histopathology. Indices of concordance (C-index) and leave-one-out cross-validation (LOOCV) were used to assess prognostic model accuracy.ResultsFive PCPR and eight CT features were treated as significant indicators in ESCC prognosis. C-indices adjusted for LOOCV were comparable among four models, 0.596 (Model A) vs. 0.658 (Model B) vs. 0.651 (Model C), and improved to 0.711with Model D combining information of clinical, CT, and histopathology (all p&lt;0.05). Using Model D, we stratified patients into low- and high-risk groups. The 3-year overall survival rates of low- and high-risk patients were 38.0% and 25.0%, respectively (p&lt;0.001).ConclusionQuantitative prognostic modeling using a combination of clinical data, histopathological, and CT images can stratify ESCC patients with surgery alone into high-risk and low-risk groups.

scholarly journals Gender differences in the maxillary sinus volume : a study using computed tomography scans of a sample of Tamil Nadu population

2016 ◽  
Vol 05 (03) ◽  
pp. 138-142
Author(s):  
N Muthukumaravel ◽  
K. Y. Manjunath
Keyword(s):  
Computed Tomography ◽  
Maxillary Sinus ◽  
Tamil Nadu ◽  
Ct Images ◽  
Ct Scans ◽  
P Value ◽  
Gender Determination ◽  
Maxillary Sinus Volume ◽  
Significant Difference ◽  
Maxillary Sinuses

Abstract Background and aims : Measurements of the maxillary sinus volumes in computed tomography (CT) scans can be used for determination of gender when other methods are inconclusive. Maxillary sinus dimension measurements are valuable in studying sexual dimorphism and can assist in gender determination. The radiographic images can provide adequate measurements for maxillary sinuses that cannot be approached by other means. The purpose of the present study was to determine and compare the volume of the maxillary sinus between males and females of Tamil Nadu region using CT scans. Materials and methods : This study was carried out by using CT images of head region of 100 males and 100 females who underwent CT scanning for indications other than the pathology of the maxillary sinuses. The CT images obtained were of patients between 20 to 50 years of age. The maxillary sinus volume of each side were calculated by using the following formula: Maximal width X Maximal height X Maximal depth X 0.5. Comparison of data between genders and sides was done. The statistical inference was derived by applying unpaired student "t" test and the p value was obtained (p value <.05 was considered statistical significant). Results: Oncomparison of males with females, the mean volumes of maxillary sinuses on each side (left and right) had shown a statistically significant difference (p<.OOOl ). The maxillary sinus volumes of the males were found to be significantly higher than that of the females. Among males, the average mean volume of maxillary sinuses (right + left) is 15.15 ± 0.45 cm3. Among females, the average mean volume of maxillary sinuses (right + left) is 12.77 ± 1.34 em' which is significantly lesser than that of the males. Conclusion : It can be concluded that the volumes of the maxillary sinuses of males are larger than those of the females and this difference is statistically significant. Maxillary sinus dimension measurements can assist in gender determination.

scholarly journals Rapid screening for COVID-19 by applying artificial intelligence to chest computed tomography images: A feasibility study

2021 ◽  
Vol 5 ◽  
pp. 239920262110136
Author(s):  
Pedro Galván ◽  
José Fusillo ◽  
Felipe González ◽  
Oraldo Vukujevic ◽  
Luciano Recalde ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Pleural Effusion ◽  
Feasibility Study ◽  
Ct Images ◽  
Public Hospitals ◽  
Rt Pcr ◽  
Chest Computed Tomography ◽  
Computed Tomography Images ◽  
Respiratory Conditions

Aim: The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay. Methods: This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country’s public hospitals. The patients’ digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine. Results: Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27–59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19–26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR. Conclusion: Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.

scholarly journals Artificial intelligence applied to non-contrast-enhanced cardiac computed tomography for the prediction of cardiovascular events

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Chiappino ◽  
D Della Latta ◽  
N Martini ◽  
A Ripoli ◽  
A Aimo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Cardiovascular Risk ◽  
General Population ◽  
Cardiovascular Events ◽  
Cardiac Computed Tomography ◽  
Ct Scans ◽  
Event Prediction ◽  
Contrast Enhanced ◽  
Asymptomatic Individuals

Abstract Background Non-contrast-enhanced cardiac computed tomography (CT) may provide two measures that are emerging as independent predictors of cardiovascular events: coronary calcium score (CCS) and the volume of epicardial fat, a metabolically and immunologically active tissue surrounding the coronary arteries. The quantification of epicardial fat volume (EFV) is not routinely performed in clinical practice for the long time required for image reconstruction and the intra- and inter-observer variability. Purpose We evaluated if artificial intelligence (AI) might prove a valuable tool to interpret the CT data-set, and to better understand the relative prognostic value of CCS and EFV compared to “traditional” cardiovascular risk factors. Methods The Montignoso HEart and Lung Project is a community-based study carried out in a small town of Northern Tuscany (Italy). Starting from 2009, asymptomatic individuals from the general population underwent a baseline screening including a non-contrast cardiac CT, and were followed-up. For the present study, CCS and EFV were automatically measured from CT scans through a deep learning (DL) strategy based on convolutional neural networks. Because of the low incidence of the primary endpoint (myocardial infarction [MI]), the observed cardiac events were predicted with a random forest model built using a subsampling approach. Results Study participants (n=1528; 48% males, age 40 to 77 years) experienced 47 MI events (3%) over 5.5±1.5 years. CCS and EFV independently predicted this endpoint (p values &lt;0.001 and 0.005, respectively) in a model including other predictors, namely weight, age, male gender, and hypertension. The model displayed a good prognostic performance, with an out-of-bag accuracy of 80.43% (accuracy on non-event prediction: 81.17%; performance on event prediction: 57,45%). The CCS emerged as the most important predictor, followed by EFV, weight and age. Interestingly, the incidence of cardiovascular events linked with CCS levels was associated with elevated EFV and the subjects with elevated CCS values but low EFV had no events (figure 1). Conclusions The tools of AI allow to perform an automated analysis of non-contrast-enhanced CT scans, with rapid and accurate measurement of CCS and EFV through a DL approach. In asymptomatic individuals from the general population, these features are more predictive of non-fatal MI than other variables related to the cardiovascular risk, as we can be demonstrated through an application of AI. Figure 1 Funding Acknowledgement Type of funding source: None

Sign in / Sign up

Export Citation Format

Share Document