| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000434092 |
| 005 | | 20200226140804 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085567794 |
| 035 | |
▼a (MiAaPQ)AAI13814483 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 362.1 |
| 100 | 1 |
▼a Chedid, Nicholas. |
| 245 | 10 |
▼a Medically Applied Artificial Intelligence: From Bench to Bedside. |
| 260 | |
▼a [S.l.]:
▼b Yale University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 67 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-02, Section: B. |
| 500 | |
▼a Advisor: Taylor, Richard A. |
| 502 | 1 |
▼a Thesis (M.D.)--Yale University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a The 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. |
| 590 | |
▼a School code: 0265. |
| 650 | 4 |
▼a Medicine. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Medical imaging. |
| 650 | 4 |
▼a Public health. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Health care management. |
| 690 | |
▼a 0564 |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0574 |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0769 |
| 690 | |
▼a 0573 |
| 710 | 20 |
▼a Yale University.
▼b Yale School of Medicine. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-02B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0265 |
| 791 | |
▼a M.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490791
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |