scholarly journals Innovative application of artificial intelligence to pre-screen COVID-19 from digital chest radiographs – our experience in a tertiary care setup

Author(s):  
Pranav Ajmera ◽  
Amit Kharat ◽  
Deepak Patkar ◽  
Mitusha Verma
2019 ◽  
pp. jramc-2018-001055
Author(s):  
Debraj Sen ◽  
R Chakrabarti ◽  
S Chatterjee ◽  
D S Grewal ◽  
K Manrai

Artificial intelligence (AI) involves computational networks (neural networks) that simulate human intelligence. The incorporation of AI in radiology will help in dealing with the tedious, repetitive, time-consuming job of detecting relevant findings in diagnostic imaging and segmenting the detected images into smaller data. It would also help in identifying details that are oblivious to the human eye. AI will have an immense impact in populations with deficiency of radiologists and in screening programmes. By correlating imaging data from millions of patients and their clinico-demographic-therapy-morbidity-mortality profiles, AI could lead to identification of new imaging biomarkers. This would change therapy and direct new research. However, issues of standardisation, transparency, ethics, regulations, training, accreditation and safety are the challenges ahead. The Armed Forces Medical Services has widely dispersed units, medical echelons and roles ranging from small field units to large static tertiary care centres. They can incorporate AI-enabled radiological services to subserve small remotely located hospitals and detachments without posted radiologists and ease the load of radiologists in larger hospitals. Early widespread incorporation of information technology and enabled services in our hospitals, adequate funding, regular upgradation of software and hardware, dedicated trained manpower to manage the information technology services and train staff, and cyber security are issues that need to be addressed.


1996 ◽  
Author(s):  
Christiaan M. Fivez ◽  
Patrick Wambacq ◽  
Paul Suetens ◽  
Emile P. Schoeters

1991 ◽  
Vol 32 (6) ◽  
pp. 442-448 ◽  
Author(s):  
M. Kehler ◽  
U. Albrechtsson ◽  
A. Andrésdóttir ◽  
P. Hochbergs ◽  
H. Lárusdóttir ◽  
...  

Inverted (positive) digital chest radiographs of patients with lung tumors were compared with commonly used (negative) digital images, consisting of one simulated normal and one contrast enhanced image. The first part of the material consisted of 80 patients of whom 40 had tumors and 40 were normal. Five radiologists with different experience reviewed the examinations. From their answers, ROC curves were constructed. The second part of the material consisted of 100 chest phantom examinations with a simulated tumor in the mediastinum (45 examinations) and/or the left lung (46 examinations). In 31 exposures there was no abnormality. These were reviewed by 3 observers and performed as an ROC study as well. There was no statistical difference between the different types of images or between the observers in the 2 studies.


1996 ◽  
Author(s):  
Jacob K. Laading ◽  
Valen E. Johnson ◽  
Alan H. Baydush ◽  
Carey E. Floyd, Jr.

1993 ◽  
Vol 20 (4) ◽  
pp. 975-982 ◽  
Author(s):  
Xuan Chen ◽  
Kunio Doi ◽  
Shigehiko Katsuragawa ◽  
Heber MacMahon

1990 ◽  
Vol 25 (8) ◽  
pp. 902-907 ◽  
Author(s):  
DAVID A. YOCKY ◽  
GEORGE W. SEELEY ◽  
THERON W. OVITT ◽  
HANS ROEHRIG ◽  
WILLIAM J. DALLAS

Author(s):  
Peikai Yan ◽  
Shaohua Li ◽  
Zhou Zhou ◽  
Qian Liu ◽  
Jiahui Wu ◽  
...  

OBJECTIVE Little is known about the efficacy of using artificial intelligence to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicenter study was aimed to establish an artificial intelligence system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study Design: Multicentre case-control study Setting: Six tertiary care centers Participants: The laryngoscopy images were collected from 2179 patients with vocal lesions. Outcome Measures: An automatic detection system of laryngeal carcinoma was established based on Faster R-CNN, which was used to distinguish vocal malignant and benign lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathology was the gold standard to identify malignant and benign vocal lesions. Results: Among 89 cases of the malignant group, the classifier was able to evaluate the laryngeal carcinoma in 66 patients (74.16%, sensitivity), while the classifier was able to assess the benign laryngeal lesion in 503 cases among 640 cases of the benign group (78.59%, specificity). Furthermore, the CNN-based classifier achieved an overall accuracy of 78.05% with a 95.63% negative prediction for the testing dataset. Conclusion: This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis, which may improve and standardize the diagnostic capacity of endoscopists using different laryngoscopes.


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