AI Chest 4 All

Thammarach, P.; Khaengthanyakan, S.; Vongsurakrai, S.; Phienphanich, Phongphan; Pooprasert, Pakinee; Yaemsuk, A.; Vanichvarodom, P.; Munpolsri, N.; Khwayotha, S.; Lertkowit, M.; Tungsagunwattana, Sutarat; Vijitsanguan, Chomphunut; Lertrojanapunya, S.; Noisiri, W.; Chiawiriyabunya, Isara; Aphikulvanich, N.; Tantibundhit, Charturong

doi:10.1109/embc44109.2020.9175862

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The utilization of deep learning on chest X-rays for lung-related disease classification has been widely studied, especially in recent years, due to interest in the efficient detection of COVID-19 [ 22 ]. The application of various well-known models such as VGG [ 23 ], Resnet [ 24 , 25 ], Inception [ 26 ], Mobilenet [ 27 ] and DenseNet [ 28 , 29 , 30 ], as well as models incorporating attention [ 31 ], pretraining [ 32 ] and ensembling [ 33 ] have been studied. Many have shown that segmenting the lung area before classification was able to improve model performance [ 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times

Yu,

Chiu,

Chang

et al. 2024

Sensors

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Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2–1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

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Section: Related Workmentioning

confidence: 99%

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times

Yu,

Chiu,

Chang

et al. 2024

Sensors

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“…Even promising results of AI models with high accuracy metrics are associated with limited specificity for the classification of the particular findings. Thus, it can barely be a substitute for a radiologist [ 7 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

An International Non-Inferiority Study for the Benchmarking of AI for Routine Radiology Cases: Chest X-ray, Fluorography and Mammography

Arzamasov¹,

Vasilev

Vladzymyrskyy

et al. 2023

Healthcare

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An international reader study was conducted to gauge an average diagnostic accuracy of radiologists interpreting chest X-ray images, including those from fluorography and mammography, and establish requirements for stand-alone radiological artificial intelligence (AI) models. The retrospective studies in the datasets were labelled as containing or not containing target pathological findings based on a consensus of two experienced radiologists, and the results of a laboratory test and follow-up examination, where applicable. A total of 204 radiologists from 11 countries with various experience performed an assessment of the dataset with a 5-point Likert scale via a web platform. Eight commercial radiological AI models analyzed the same dataset. The AI AUROC was 0.87 (95% CI:0.83–0.9) versus 0.96 (95% CI 0.94–0.97) for radiologists. The sensitivity and specificity of AI versus radiologists were 0.71 (95% CI 0.64–0.78) versus 0.91 (95% CI 0.86–0.95) and 0.93 (95% CI 0.89–0.96) versus 0.9 (95% CI 0.85–0.94) for AI. The overall diagnostic accuracy of radiologists was superior to AI for chest X-ray and mammography. However, the accuracy of AI was noninferior to the least experienced radiologists for mammography and fluorography, and to all radiologists for chest X-ray. Therefore, an AI-based first reading could be recommended to reduce the workload burden of radiologists for the most common radiological studies such as chest X-ray and mammography.

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AI Chest 4 All

Cited by 2 publications

References 16 publications

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times

An International Non-Inferiority Study for the Benchmarking of AI for Routine Radiology Cases: Chest X-ray, Fluorography and Mammography

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