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Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development [article]

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy Wu, Arjun Sharma, Matthew Tong, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth A Krupinski, Mehdi Moradi
2020 arXiv   pre-print
We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning / machine learning methods.  ...  The data were collected using an eye tracking system while a radiologist reviewed and reported on 1,083 CXR images.  ...  was used to extract 17 anatomical bounding boxes for each CXR image, which include: 'right lung', 'right upper lung zone', 'right mid lung zone', 'right lower lung zone', 'left lung', 'left upper lung  ... 
arXiv:2009.07386v3 fatcat:br6qkfve6vdtzgmlviklucytmi

PadChest: A large chest x-ray image dataset with multi-label annotated reports [article]

Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, Maria de la Iglesia-Vayá
2019 arXiv   pre-print
Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms.  ...  To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish  ...  The Medical Image Bank of the Valencian Community as well as de-identification and anonymization services, were partially funded by the Regional Ministry of Health (FEDER program) and the Horizon 2020  ... 
arXiv:1901.07441v2 fatcat:uuhka6akyrhr7orlppbgymxjsy

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy T. Wu, Arjun Sharma, Matthew Tong, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth A. Krupinski, Mehdi Moradi
2021 Scientific Data  
We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods.  ...  The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images.  ...  This dataset consists of eye gaze information recorded from a single radiologist interpreting frontal chest radiographs.  ... 
doi:10.1038/s41597-021-00863-5 pmid:33767191 fatcat:5ilyo5cngffzth46lgc5ypj56i

Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs

Tao Xu, Irene Cheng, Richard Long, Mrinal Mandal
2013 EURASIP Journal on Image and Video Processing  
Cavities in the upper lung zone provide a useful cue to radiologists for potential infectious TB.  ...  However, the superimposed anatomical structures in the lung field hinder effective identification of these cavities.  ...  Introduction Chest radiographs or chest X-ray (CXR) images are widely used to diagnose lung diseases such as lung cancer, tuberculosis (TB), and pneumonia.  ... 
doi:10.1186/1687-5281-2013-3 fatcat:3smuqpcrczhf7mo3qels6nxk4u

Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays

Manohar Karki, Karthik Kantipudi, Feng Yang, Hang Yu, Yi Xiang J. Wang, Ziv Yaniv, Stefan Jaeger
2022 Diagnostics  
A comparison of radiologist-annotated lesion locations in the lung and the trained model's localization of areas of interest, using GradCAM, did not show much overlap.  ...  Classification of drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) from chest radiographs remains an open problem.  ...  Classic machine learning algorithms and CNNs with pretrained weights were used on the clinical text data and chest X-ray images respectively.  ... 
doi:10.3390/diagnostics12010188 pmid:35054355 pmcid:PMC8775073 fatcat:siyebe3s5zes7ftxj2bnqo3ygu

AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia [article]

Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard (+19 others)
2020 arXiv   pre-print
AI-driven combination of variables with CT-based biomarkers offers perspectives for optimal patient management given the shortage of intensive care beds and ventilators.  ...  performance of experts and data-driven identification of biomarkers for its prognosis.  ...  The overall annotation effort took approximately 800 hours and involved 15 radiologists with 1 to 7 years of experience in chest imaging.  ... 
arXiv:2004.12852v1 fatcat:zrlolg4lpjb3tavvknncsa7jze

AI INFLUENCE IN COVID-19 DETECTION

Cijil Benny, MSc Data Science, Department of Mathematics, Chandigarh University, Gharuan-Mohali, Punjab-140413, India.
2021 Journal of University of Shanghai for Science and Technology  
This paper is on analyzing the feasibility of AI studies and the involvement of AI in COVID interrelated treatments. In all, several procedures were reviewed and studied. It was on point.  ...  They include medical and case reports, medical strategies, and persons respectively. Approaches are being done through shared statistical analysis based on these reports.  ...  Support vector machines Vector support machines (SVMs) are overseen methods of reversion, distribution and extra outlier identification for achievement methods.  ... 
doi:10.51201/jusst/21/07235 fatcat:alglno7cg5ew7irvtxrszycpjy

Holistic AI-Driven Quantification, Staging and Prognosis of COVID-19 Pneumonia [article]

Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard (+23 others)
2020 medRxiv   pre-print
This approach relies on automatic computed tomography (CT)-based disease quantification using deep learning, robust data-driven identification of physiologically-inspired COVID-19 holistic patient profiling  ...  In this letter, we report on an artificial intelligence solution for performing automatic staging and prognosis based on imaging, clinical, comorbidities and biological data.  ...  The overall annotation effort took approximately 800 hours and involved 15 radiologists with 1 to 7 years of experience in chest imaging.  ... 
doi:10.1101/2020.04.17.20069187 fatcat:gq3burxzxnhxxg7dcg22vnj4du

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Toufique A. Soomro, Lihong Zheng, Ahmed J. Afifi, Ahmed Ali, Ming Yin, Junbin Gao
2021 Artificial Intelligence Review  
AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data  ...  This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis.  ...  For example, many methods are implemented based on the two-step process for diagnosing COVID-19 and in which the entire lung region is segmented using machine learning methods.  ... 
doi:10.1007/s10462-021-09985-z pmid:33875900 pmcid:PMC8047522 fatcat:rxh57na6bzburm6ekao2ddz4ku

Artificial Intelligence-Driven Assessment of Radiological Images for COVID-19

Yassine Bouchareb, Pegah Moradi Khaniabadi, Faiza Al Kindi, Humoud Al Dhuhli, Isaac Shiri, Habib Zaidi, Arman Rahmim
2021 Computers in Biology and Medicine  
In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated.  ...  and validation as appropriate for AI-based COVID-19 studies.  ...  The authors reported that this auto contoured regions could assist radiologists for their annotation refinements. Song et al.  ... 
doi:10.1016/j.compbiomed.2021.104665 pmid:34343890 pmcid:PMC8291996 fatcat:nstm5p56rbgf5pd6hei42iia34

Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

Joseph Bae, Saarthak Kapse, Gagandeep Singh, Rishabh Gattu, Syed Ali, Neal Shah, Colin Marshall, Jonathan Pierce, Tej Phatak, Amit Gupta, Jeremy Green, Nikhil Madan (+1 others)
2021 Diagnostics  
In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients.  ...  All results are compared against radiologist grading of CXRs (zone-wise expert severity scores).  ...  instituted by the Office of the Dean and supported by the Department of Biomedical Informatics.  ... 
doi:10.3390/diagnostics11101812 pmid:34679510 fatcat:lte44gvsobfrzgeeympsu7eahu

Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

May Phu Paing, Kazuhiko Hamamoto, Supan Tungjitkusolmun, Chuchart Pintavirooj
2019 Applied Sciences  
Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide.  ...  In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding.  ...  We would also like to thank The Cancer Imaging Archive (TCIA) for publically sharing the CT scan images and clinical data which were applied in our experiments.  ... 
doi:10.3390/app9112329 fatcat:dw32frowfvcmvk6vmp45nnhk6q

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, George D. C. Cavalcanti, Yandre M. G. Costa
2021 Sensors  
The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification.  ...  Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most.  ...  Acknowledgments: We appreciate the effort of Joseph Paul Cohen from the University of Montreal for maintaining a repository of COVID-19 images for the research community.  ... 
doi:10.3390/s21217116 pmid:34770423 pmcid:PMC8587284 fatcat:v7ssv6p3bnbyrhba7f2wjmcb6u

Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images [article]

Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, George D. C. Cavalcanti, Yandre M. G. Costa
2021 arXiv   pre-print
The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification.  ...  Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most.  ...  From this point, we focus on works devoted to COVID-19 identification using chest images that somehow dealt with the identification of regions of interest.  ... 
arXiv:2009.09780v4 fatcat:ax5bidpv65g2xpqgjwoqm3uuvy

Application of Artificial Intelligence in Lung Cancer

Hwa-Yen Chiu, Heng-Sheng Chao, Yuh-Min Chen
2022 Cancers  
Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection.  ...  Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer.  ...  Acknowledgments: Thanks to the Ministry of Science and Technology, Taiwan, for funding. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers14061370 pmid:35326521 pmcid:PMC8946647 fatcat:4xajyblwpzh3ddwr7ut6n32ov4
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