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

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Abstract

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.

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Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Nanshan Chen, Min Zhou, Xuan Dong … (2022)

Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019-nCoV pneumonia. Methods In this retrospective, single-centre study, we included all confirmed cases of 2019-nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real-time RT-PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019-nCoV pneumonia, 49 (49%) had a history of exposure to the Huanan seafood market. The average age of the patients was 55·5 years (SD 13·1), including 67 men and 32 women. 2019-nCoV was detected in all patients by real-time RT-PCR. 50 (51%) patients had chronic diseases. Patients had clinical manifestations of fever (82 [83%] patients), cough (81 [82%] patients), shortness of breath (31 [31%] patients), muscle ache (11 [11%] patients), confusion (nine [9%] patients), headache (eight [8%] patients), sore throat (five [5%] patients), rhinorrhoea (four [4%] patients), chest pain (two [2%] patients), diarrhoea (two [2%] patients), and nausea and vomiting (one [1%] patient). According to imaging examination, 74 (75%) patients showed bilateral pneumonia, 14 (14%) patients showed multiple mottling and ground-glass opacity, and one (1%) patient had pneumothorax. 17 (17%) patients developed acute respiratory distress syndrome and, among them, 11 (11%) patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019-nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019-nCoV infection. Funding National Key R&D Program of China.

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Cancer statistics, 2022

Rebecca Siegel, Kimberly D Miller, Hannah Fuchs … (2022)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.

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Reduced lung-cancer mortality with low-dose computed tomographic screening.

(2011)

The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer. From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02). Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).

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Author and article information

Contributors

Masahiro Yanagawa:

ORCID: http://orcid.org/0000-0002-0911-6769

m-yanagawa@radiol.med.osaka-u.ac.jp

Journal

Journal ID (nlm-ta): Radiol Med

Journal ID (iso-abbrev): Radiol Med

Title: La Radiologia Medica

Publisher: Springer Milan (Milan )

ISSN (Print): 0033-8362

ISSN (Electronic): 1826-6983

Publication date (Electronic): 28 August 2023

Publication date PMC-release: 28 August 2023

Publication date (Print): 2023

Volume: 128

Issue: 10

Pages: 1236-1249

Affiliations

[1 ]Department of Radiology, Osaka University Graduate School of Medicine, ( https://ror.org/035t8zc32) 2-2 Yamadaoka, Suita-City, Osaka 565-0871 Japan

[2 ]Department of Radiology, Nagoya University Graduate School of Medicine, ( https://ror.org/04chrp450) 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8550 Japan

[3 ]Department of Radiology, Keio University School of Medicine, ( https://ror.org/02kn6nx58) 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016 Japan

[4 ]Department of Diagnostic Radiology, Tokyo Medical and Dental University, ( https://ror.org/051k3eh31) 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519 Japan

[5 ]GRID grid.263518.b, ISNI 0000 0001 1507 4692, Department of Radiology, , Shinshu University School of Medicine, ; 3-1-1 Asahi, Matsumoto, Nagano 390-2621 Japan

[6 ]Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, ( https://ror.org/057zh3y96) 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655 Japan

[7 ]Department of Radiology, Juntendo University Graduate School of Medicine, ( https://ror.org/01692sz90) Bunkyo-ku, Tokyo, 113-8421 Japan

[8 ]Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, ( https://ror.org/02kpeqv85) 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507 Japan

[9 ]Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, ( https://ror.org/02pc6pc55) 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558 Japan

[10 ]Department of Diagnostic Radiology, Hiroshima University, ( https://ror.org/03t78wx29) 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551 Japan

[11 ]Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, ( https://ror.org/01hvx5h04) 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585 Japan

[12 ]GRID grid.412167.7, ISNI 0000 0004 0378 6088, Department of Diagnostic and Interventional Radiology, , Hokkaido University Hospital, ; N15, W5, Kita-ku, Sapporo, 060-8638 Japan

[13 ]Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, ( https://ror.org/02cgss904) 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556 Japan

[14 ]Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, ( https://ror.org/02e16g702) Kita 15 Nish I 7, Kita-ku, Sapporo, Hokkaido 060-8648 Japan

Author information

Masahiro Yanagawa http://orcid.org/0000-0002-0911-6769

Article

Publisher ID: 1691

DOI: 10.1007/s11547-023-01691-w

PMC ID: 10547663

PubMed ID: 37639191

SO-VID: 15c5d781-f324-4c7c-b0f4-a571d797343b

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 2 July 2023

Date accepted : 25 July 2023

Funding

Funded by: Osaka University

Open Access :

Open access funding provided by Osaka University.

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Keywords: artificial intelligence,deep learning,convolutional neural network,vision transformer,explainable ai,thoracic imaging

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Keywords: artificial intelligence, deep learning, convolutional neural network, vision transformer, explainable ai, thoracic imaging

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

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Most cited references 110

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Cancer statistics, 2022

Reduced lung-cancer mortality with low-dose computed tomographic screening.

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