Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution

被引：6

作者：

Dasegowda, Giridhar ^{[1
,2
,3
]}

Kalra, Mannudeep K. ^{[1
,2
,3
]}

Abi-Ghanem, Alain S. ^{[4
]}

Arru, Chiara D. ^{[5
]}

Bernardo, Monica ^{[6
,7
]}

Saba, Luca ^{[8
]}

Segota, Doris ^{[9
]}

Tabrizi, Zhale

Viswamitra, Sanjaya ^{[10
]}

Kaviani, Parisa ^{[1
,2
,3
]}

Karout, Lina ^{[1
,2
,3
]}

Dreyer, Keith J. ^{[1
,2
,3
]}

机构：

[1] Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA

[2] Harvard Med Sch, Boston, MA 02114 USA

[3] Mass Gen Brigham Data Sci Off DSO, Boston, MA 02114 USA

[4] Amer Univ, Dept Diagnost Radiol, Beirut Med Ctr, Beirut 110236, Lebanon

[5] Azienda Osped G Brotzu, Dept Radiol, I-09134 Cagliari, Italy

[6] UNIMED, Hosp Miguel Soeiro, Dept Radiol, BR-18052210 Sorocaba, Brazil

[7] Pontificia Univ Catholic Sao Paulo, Dept Radiol, BR-05014901 Sao Paulo, Brazil

[8] Azienda Osped Univ Cagliari, Dept Radiol, I-09123 Cagliari, Italy

[9] Clin Hosp Ctr Rijeka, Med Phys & Radiat Protect Dept, Rijeka 51000, Croatia

[10] Iran Univ Med Sci, Radiol Dept, Tehran 560066, Iran

来源：

DIAGNOSTICS | 2023年 / 13卷 / 03期

关键词：

artificial intelligence; chest X-ray; computer-assisted image processing; quality improvement; radiography; DIGITAL RADIOGRAPHY; REJECT ANALYSIS; TRENDS;

D O I：

10.3390/diagnostics13030412

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs.

引用

页数：10

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