scholarly journals A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

2021 ◽  
Author(s):  
Chenggong Yan ◽  
Lingfeng Wang ◽  
Jie Lin ◽  
Jun Xu ◽  
Tianjing Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Pulmonary Tuberculosis ◽  
Image Analysis System ◽  
Ct Image ◽  
Accurate Detection ◽  
Fully Automatic ◽  
Ct Image Analysis ◽  
Analysis System

scholarly journals A Fully Automatic AI-based CT Image Analysis System for Accurate Detection, Diagnosis, and Quantitative Severity Evaluation of Pulmonary Tuberculosis

2021 ◽  
Author(s):  
Chenggong Yan ◽  
Lingfeng Wang ◽  
Jie Lin ◽  
Jun Xu ◽  
Tianjing Zhang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Pulmonary Tuberculosis ◽  
Chest Ct ◽  
Ct Scans ◽  
Image Analysis System ◽  
Imaging Features ◽  
Ct Image ◽  
Pulmonary Tb ◽  
Ct Image Analysis ◽  
Analysis System

Abstract Background: Accurate and rapid diagnosis of pulmonary tuberculosis (TB) plays a crucial role in timely prevention and appropriate medical treatment to the disease. This study aims to develop and evaluate an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB.Methods: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning based cascading framework was connected to create a processing pipeline. To train and validate the model, 1921 lesions were manually labeled, classified by six categories of critical imaging features, and visually scored for the lesion involvement as the ground truth. “TB score” was calculated by the network-activation map to assess the disease burden quantitively. Independent test datasets from two additional hospitals and NIH TB Portal were used to validate externally the performance of the AI model.Results: CT scans from 526 participants (mean age, 48.5 years±16.5; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of 0.68 on the validation cohort. In the independent datasets, the overall classification accuracy for six pulmonary critical imaging findings indicative of TB were 81.08%-91.05%. A moderate to strong correlation was demonstrated between the AI model quantified “TB score” and the radiologist-estimated CT score.Conclusion: This end-to-end AI system based on chest CT can achieve human-level diagnostic performance, and holds great potential for early management and medical resource optimization of patients with pulmonary TB in clinical practice.

scholarly journals Comparison of G-guide and Image-free Navigation System in Accuracy of Stem Anteversion Assessment During Total Hip Arthroplasty

2019 ◽  
Vol 13 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Yuki Fujihara ◽  
Shigeo Fukunishi ◽  
Tomokazu Fukui ◽  
Shoji Nishio ◽  
Yu Takeda ◽  
...  
Keyword(s):  
Image Analysis ◽  
Navigation System ◽  
Measurement Accuracy ◽  
Femoral Stem ◽  
Reference Value ◽  
Image Analysis System ◽  
Measurement Systems ◽  
Comparative Accuracy ◽  
Ct Image Analysis ◽  
Analysis System

Introduction: We have developed and utilized the Gravity-guide (G-guide) as a simple manual instrument for intraoperative assessment and adjustment of stem anteversion (AV). Since 2013, we simultaneously measured stem AV using the G-guide and image-free navigation during THA procedure. The purpose of this study was to compare the measurement accuracy of the G-guide and navigation system using the postoperative CT results as a reference. Methods: In total, 59 hips in 56 patients who underwent primary THA using both the G-guide and image-free navigation system were included in the study. All patients underwent postoperative CT examination, and the femoral stem AV was assessed using a 3D image analysis system (Zed hip, LEXI, Japan). The AV angle derived from the postoperative CT image analysis was used as the reference value to assess the accuracy of the two intraoperative measurement systems. Results: The discrepancy between the G-guide and the postoperative CT-measured values averaged 5.0° ± 3.9°, while the corresponding value for the navigation system was 5.2° ± 4.1°. Acceptable accuracy with a measurement error of less than 10° was achieved in 86% and 90% of the cases for the G-guide and navigation measurements respectively. Conclusion: Consequently, it was shown that both navigation and G-guide measurements can achieve comparative accuracy and are clinically useful.

scholarly journals Chest CT Images for COVID-19: Radiologists and Computer-based detection

2020 ◽  
Author(s):  
Qingli Dou ◽  
Jiangping Liu ◽  
Wenwu Zhang ◽  
Yanan Gu ◽  
Wan-Ting Hsu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Image Analysis System ◽  
Ct Image ◽  
Chest Computed Tomography ◽  
Computer Based ◽  
Analysis System ◽  
Novel Coronavirus

ABSTRACTBackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI system, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionsWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis

2020 ◽  
Vol 91 ◽  
pp. 103585 ◽  
Author(s):  
Francisco F.X. Vasconcelos ◽  
Róger M. Sarmento ◽  
Pedro P. Rebouças Filho ◽  
Victor Hugo C. de Albuquerque
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Ct Image ◽  
Artificial Intelligence Techniques ◽  
Brain Ct ◽  
Cloud Architecture ◽  
Ct Image Analysis

Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies

2019 ◽  
Vol 8 (4) ◽  
pp. 38-54 ◽  
Author(s):  
Abraham Pouliakis ◽  
Niki Margari ◽  
Effrosyni Karakitsou ◽  
George Valasoulis ◽  
Nektarios Koufopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
System Performance ◽  
Malignant Disease ◽  
Competitive Learning ◽  
Image Analysis System ◽  
Morphometric Features ◽  
Analysis System ◽  
Histological Confirmation

Objective of this study is to investigate the potential of an artificial intelligence (AI) technique, based on competitive learning, for the discrimination of benign from malignant endometrial nuclei and lesions. For this purpose, 416 liquid-based cytological smears with histological confirmation were collected, each smear corresponded to one patient. From each smear was extracted nuclear morphometric features by the application of an image analysis system. Subsequently nuclei measurement from 50% of the cases were used to train the AI system to classify each individual nucleus as benign or malignant. The remaining measurement, from the unused 50% of the cases, were used for AI system performance evaluation. Based on the results of nucleus classification the patients were discriminated as having benign or malignant disease by a secondary subsystem specifically trained for this purpose. Based on the results it was conclude that AI based computerized systems have the potential for the classification of both endometrial nuclei and lesions.

scholarly journals Chest CT Images for COVID-19: Radiologists and Computer-Based Detection

2021 ◽  
Vol 8 ◽  
Author(s):  
Qingli Dou ◽  
Jiangping Liu ◽  
Wenwu Zhang ◽  
Yanan Gu ◽  
Wan-Ting Hsu ◽  
...  
Keyword(s):  
Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Viral Pneumonia ◽  
Image Analysis System ◽  
Ct Image ◽  
Significant Difference ◽  
Computer Based ◽  
Analysis System ◽  
Novel Coronavirus

BackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

Chemical Classification and Particle Sizing of Foundry Sands

1985 ◽  
Vol 43 ◽  
pp. 388-389
Author(s):  
D.S. DeMiglio
Keyword(s):  
Image Analysis ◽  
Energy Dispersive Spectrometer ◽  
Image Analysis System ◽  
Representative Sampling ◽  
Foundry Sands ◽  
Large Atomic Number ◽  
Backscattered Electron ◽  
Sand Particles ◽  
Analysis System ◽  
Reclamation System

Much progress has been made in recent years towards the development of closed-loop foundry sand reclamation systems. However, virtually all work to date has determined the effectiveness of these systems to remove surface clay and metal oxide scales by a qualitative inspection of a representative sampling of sand particles. In this investigation, particles from a series of foundry sands were sized and chemically classified by a Lemont image analysis system (which was interfaced with an SEM and an X-ray energy dispersive spectrometer) in order to statistically document the effectiveness of a reclamation system developed by The Pangborn Company - a subsidiary of SOHIO.The following samples were submitted: unreclaimed sand; calcined sand; calcined & mechanically scrubbed sand and unused sand. Prior to analysis, each sample was sprinkled onto a carbon mount and coated with an evaporated film of carbon. A backscattered electron photomicrograph of a field of scale-covered particles is shown in Figure 1. Due to a large atomic number difference between sand particles and the carbon mount, the backscattered electron signal was used for image analysis since it had a uniform contrast over the shape of each particle.

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