New trend in artificial intelligence-based assistive technology for thoracic imaging

被引：28

作者：

Yanagawa, Masahiro ^{[1
]}

Ito, Rintaro ^{[2
]}

Nozaki, Taiki ^{[3
]}

Fujioka, Tomoyuki ^{[4
]}

Yamada, Akira ^{[5
]}

Fujita, Shohei ^{[6
,7
]}

Kamagata, Koji ^{[8
]}

Fushimi, Yasutaka ^{[9
]}

Tsuboyama, Takahiro ^{[1
]}

Matsui, Yusuke ^{[10
]}

Tatsugami, Fuminari ^{[11
]}

Kawamura, Mariko ^{[2
]}

Ueda, Daiju ^{[12
]}

Fujima, Noriyuki ^{[13
]}

Nakaura, Takeshi ^{[14
]}

Hirata, Kenji ^{[15
]}

Naganawa, Shinji ^{[2
]}

机构：

[1] Osaka Univ, Grad Sch Med, Dept Radiol, 2-2 Yamadaoka, Suita, Osaka 5650871, Japan

[2] Nagoya Univ, Grad Sch Med, Dept Radiol, 65 Tsurumai Cho,Showa Ku, Nagoya, Aichi 4668550, Japan

[3] Keio Univ, Sch Med, Dept Radiol, 35 Shinanomachi,Shinjuku Ku, Tokyo 1600016, Japan

[4] Tokyo Med & Dent Univ, Dept Diagnost Radiol, 1-5-45 Yushima,Bunkyo Ku, Tokyo 1138519, Japan

[5] Shinshu Univ, Sch Med, Dept Radiol, 3-1-1 Asahi, Matsumoto, Nagano 3902621, Japan

[6] Univ Tokyo, Grad Sch Med, Dept Radiol, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138655, Japan

[7] Univ Tokyo, Fac Med, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138655, Japan

[8] Juntendo Univ, Grad Sch Med, Dept Radiol, Bunkyo Ku, Tokyo 1138421, Japan

[9] Kyoto Univ, Grad Sch Med, Dept Diagnost Imaging & Nucl Med, 54 Shogoin Kawaharacho,Sakyoku, Kyoto 6068507, Japan

[10] Okayama Univ, Fac Med Dent & Pharmaceut Sci, Dept Radiol, 2-5-1 Shikata Cho,Kita Ku, Okayama 7008558, Japan

[11] Hiroshima Univ, Dept Diagnost Radiol, 1-2-3 Kasumi,Minami Ku, Hiroshima 7348551, Japan

[12] Osaka Metropolitan Univ, Grad Sch Med, Dept Diagnost & Intervent Radiol, 1-4-3 Asahi Machi,Abeno Ku, Osaka 5458585, Japan

[13] Hokkaido Univ Hosp, Dept Diagnost & Intervent Radiol, N15,W5,Kita Ku, Sapporo, Hokkaido 0608638, Japan

[14] Kumamoto Univ, Grad Sch Med, Dept Diagnost Radiol, 1-1-1 Honjo Chuo Ku, Kumamoto 8608556, Japan

[15] Hokkaido Univ, Grad Sch Med, Dept Diagnost Imaging, Kita 15 Nish 7,Kita Ku, Sapporo, Hokkaido 0608648, Japan

来源：

RADIOLOGIA MEDICA | 2023年 / 128卷 / 10期

关键词：

Artificial intelligence; Deep learning; Convolutional neural network; Vision transformer; Explainable AI; Thoracic imaging; SUSPECTED PULMONARY-EMBOLISM; SECTION COMPUTED-TOMOGRAPHY; FORTHCOMING 8TH EDITION; IASLC LUNG-CANCER; VOLUMETRIC MEASUREMENTS; TNM CLASSIFICATION; NODULES; CT; PERFORMANCE; PROPOSALS;

D O I：

10.1007/s11547-023-01691-w

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

引用

页码：1236 / 1249

页数：14

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