자료유형  

 학위논문

Control Number  

0015490791

International Standard Book Number  

9781085567794

Dewey Decimal Classification Number  

362.1

Main Entry-Personal Name  

Chedid, Nicholas.

Publication, Distribution, etc. (Imprint  

[Sl] : Yale University, 2019

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2019

Physical Description  

67 p

General Note  

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

General Note  

Advisor: Taylor, Richard A.

Dissertation Note  

Thesis (M.D.)--Yale University, 2019.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Restrictions on Access Note  

This item must not be added to any third party search indexes.

Summary, Etc.  

요약The intent of this thesis was to develop several medically applied artificial intelligence programs, which can be considered either clinical decision support tools or programs which make the development of such tools more feasible. The first two projects are more basic or "bench" in focus, while the final project is more translational. The first program involves the creation of a residual neural network to automatically detect the presence of pericardial effusions in point-of-care echocardiography and currently has an accuracy of 71%. The second program involves the development of a sub-type of generative adverserial network to create synthetic x-rays of fractures for several purposes including data augmentation for the training of a neural network to automatically detect fractures. We have already generated high quality synthetic x-rays. Weare currently using structural similarity index measurements and Visual Turing tests with three radiologists in order to further evaluate image quality. The final project involves the development of neural networks for audio and visual analysis of 30 seconds of video to diagnose and monitor treatment of depression. Our current root mean square error (RMSE) is 9.53 for video analysis and 11.6 for audio analysis, which are currently second best in the literature and still improving. Clinical pilot studies for this final project are underway. The gathered clinical data will be first-in-class and orders of magnitude greater than other related datasets and should allow our accuracy to be best in the literature. We are currently applying for a translational NIH grant based on this work.

Subject Added Entry-Topical Term  

Medicine

Subject Added Entry-Topical Term  

Computer science

Subject Added Entry-Topical Term  

Medical imaging

Subject Added Entry-Topical Term  

Public health

Subject Added Entry-Topical Term  

Artificial intelligence

Subject Added Entry-Topical Term  

Health care management

Added Entry-Corporate Name  

Yale University Yale School of Medicine

Host Item Entry  

Dissertations Abstracts International. 81-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:566994