Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT

2021 ◽  
Author(s):  
Basel Yacoub ◽  
Ismail M. Kabakus ◽  
U. Joseph Schoepf ◽  
Vincent M. Giovagnoli ◽  
Andreas M. Fischer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chest Ct ◽  
Radiology Reports ◽  
Clinical Radiology

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening

2021 ◽  
Vol 17 ◽  
Author(s):  
Yaping Zhang ◽  
Beibei Jiang ◽  
Lu Zhang ◽  
Marcel J.W. Greuter ◽  
Geertruida H. de Bock ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Detection Rate ◽  
Lung Nodule ◽  
Chest Ct ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Radiology Reports ◽  
Nodule Detection ◽  
Assisted Reading

Background: Artificial intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of AI system by comparing AI-assisted nodule detection with actu-al radiology reports. Objective: To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening. Methods: Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated to finalize radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to lo-cate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods. Results: In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiolo-gists observed 131, lower than 247 by AI-assisted reading (p<0.001). The panel confirmed 111 patients with >1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively. Conclusion: Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensi-tivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules.

scholarly journals Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT

Radiology ◽  
2020 ◽  
Vol 296 (3) ◽  
pp. E156-E165 ◽  
Author(s):  
Harrison X. Bai ◽  
Robin Wang ◽  
Zeng Xiong ◽  
Ben Hsieh ◽  
Ken Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chest Ct

scholarly journals Artificial intelligence in paediatric radiology: Future opportunities

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200975
Author(s):  
Natasha Davendralingam ◽  
Neil J Sebire ◽  
Owen J Arthurs ◽  
Susan C Shelmerdine
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
Early Stage ◽  
Image Interpretation ◽  
Paediatric Radiology ◽  
Radiology Service ◽  
Clinical Radiology ◽  
The Media ◽  
Performance Statistics ◽  
Image Pattern

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children’s imaging has been hitherto neglected. In this article, we discuss a variety of possible ‘use cases’ in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a ‘future, enhanced paediatric radiology service’ could operate and to stimulate further discussion with avenues for research.

scholarly journals Application of artificial intelligence image-assisted diagnosis system in chest CT examination of COVID-19

2020 ◽  
Author(s):  
Mengqi Zhang ◽  
Yangyang Wang ◽  
Qianyun Ding ◽  
Haiwen Li ◽  
Fu Dai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ct Scan ◽  
Predictive Value ◽  
False Positive ◽  
Chest Ct ◽  
True Positive ◽  
Diagnosis System ◽  
Ct Examination ◽  
Missed Diagnosis ◽  
Assisted Diagnosis

Abstract Purpose The purpose of this study is to evaluate the application efficiency of artificial intelligence (AI) image-assisted diagnosis system in chest CT examination of corona virus disease 2019 (COVID-19). Methods A total of 33 cases of COVID-19 patients who underwent chest CT in Hefei Binhu Hospital between January 2020 and March 2020 were retrospectively included. All patients were tested positive for novel coronavirus nucleic acid by fluorescent reverse transcription-polymerasechain reaction (RT-PCR). The pneumonia screening function of the AI image-assisted diagnosis system was employed for the 103 chest CT examinations of the 33 cases. The diagnosis of four senior radiologists were used as the standard for synchronous under blind state. The sensitivity, specificity, misdiagnosis rate, missed diagnosis rate and other evaluation indexes of the COVID-19 performed by the AI image-assisted diagnosis system were analyzed, and an dynamic evaluation on the CT reexamination was conducted. Results Out of the 103 chest CT examinations, there were 88 cases of true positive, 1 case of false positive, 12 cases of true negative and 2 cases of false negative. The sensitivity was 97.78% (88/90); the specificity was 92.31% (12/13); the positive predictive value was 98.88% (88/89); the negative predictive value was 85.71% (12/14); the accuracy was 97.09% (100/103); the Youden index was 90.09%; the positive likelihood ratio was 12.711 and the negative likelihood ratio was 0.024. There were 790 identified lesions in these CT examinations in total, of which 569 were true positive and 221 were false positive. There were also 64 missed diagnosis markers. The detection rate of all lesions was 89.89% and the rate of false positives was 27.97%. In the last CT scan, the lesion size were smaller and the percentage of lesions in total lung volume along with the mean density of lesions was lower than that of the first CT scan. Conclusion The AI image-assisted diagnosis system has certain clinical application value in the early diagnosis and follow-up evaluation of chest CT examination of COVID-19.

scholarly journals AI-Based Image Processing for COVID-19 Detection in Chest CT Scan Images

2021 ◽  
Vol 2 ◽  
Author(s):  
Hussein Kaheel ◽  
Ali Hussein ◽  
Ali Chehab
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Image Processing ◽  
Chest Ct ◽  
Levels Of Analysis ◽  
Training Phase ◽  
The Right ◽  
Best Fit ◽  
Chest Ct Scan

The COVID-19 pandemic has attracted the attention of big data analysts and artificial intelligence engineers. The classification of computed tomography (CT) chest images into normal or infected requires intensive data collection and an innovative architecture of AI modules. In this article, we propose a platform that covers several levels of analysis and classification of normal and abnormal aspects of COVID-19 by examining CT chest scan images. Specifically, the platform first augments the dataset to be used in the training phase based on a reliable collection of images, segmenting/detecting the suspicious regions in the images, and analyzing these regions in order to output the right classification. Furthermore, we combine AI algorithms, after choosing the best fit module for our study. Finally, we show the effectiveness of this architecture when compared to other techniques in the literature. The obtained results show that the accuracy of the proposed architecture is 95%.

scholarly journals Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

scholarly journals Automated chest radiograph diagnosis: A Twofer for Tuberculosis and Covid-19

2020 ◽  
Author(s):  
Mitushi Verma ◽  
Deepak Patkar ◽  
Madhura Ingalharikar ◽  
Amit Kharat ◽  
Pranav Ajmera ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
X Ray ◽  
Recent Onset ◽  
Radiology Reports ◽  
Fast Spreading ◽  
Digital Chest ◽  
Chest X Ray ◽  
End To End ◽  
The Times

AbstractCoronavirus disease (Covid 19) and Tuberculosis (TB) are two challenges the world is facing. TB is a pandemic which has challenged mankind for ages and Covid 19 is a recent onset fast spreading pandemic. We study these two conditions with focus on Artificial Intelligence (AI) based imaging, the role of digital chest x-ray and utility of end to end platform to improve turnaround times. Using artificial intelligence assisted technology for triage and creation of structured radiology reports using an end to end platform can ensure quick diagnosis. Changing dynamics of TB screening in the times of Covid 19 pandemic have resulted in bottlenecks for TB diagnosis. The paper tries to outline two types of use cases, one is COVID-19 screening in a hospital-based scenario and the other is TB screening project in mobile van setting and discusses the learning of these models which have both used AI for prescreening and generating structured radiology reports.

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