• Korean Journal of Radiology
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• 제21권10호
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• pp.1150-1160
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• 2020

Objective: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. Materials and Methods: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. Results: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). Conclusion: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

• 대한방사선기술학회지:방사선기술과학
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• 제32권2호
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• pp.141-146
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• 2009

1956년부터 2005년까지 50년간 대한결핵협회의 X선 검진사업에 의해 촬영된 흉부방사선영상(CXR) 판독 결과를 분석하였으며 통계의 특성 및 자료의 원본에 충실하기 위해 대한결핵협회에서 발행되는 연보(annual report)의 내용만을 분석하였다. 따라서 결핵협회의 사업목적 중의 하나인 폐결핵 유소견자에 대한 분석이 핵심적으로 이루어졌으며 연령과 성별 등은 포함되지 않았다. 50년간의 누적 검진 대상자에 대한 폐결핵 유소견자를 질환별로 분석한 결과는 다음과 같다. 검진대상자 총 수는 54,938,875명으로 나타났다. 이 중 폐결핵 유소견자 수는 958,251명(1.74%), 요치료자 465,082명(0.85%), 경증자 229,615명(0.42%), 중등증 144,247명(0.26%), 중증 74,066명(0.13%), 삼출성흉막염 17,154명(0.03%), 요관찰자 493,169명(0.90%), 활동성 미정 78,214명(0.14%), 의사결핵 272,349명(0.50%) 등으로 나타났다.

• Korean Journal of Radiology
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• 제23권1호
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• pp.139-149
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• 2022

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

• 대한방사선기술학회지:방사선기술과학
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• 제43권5호
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• pp.397-404
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• 2020

Coronavirus disease(COVID-19) is highly infectious disease that directly affects the lungs. To observe the clinical findings from these lungs, the Chest Radiography(CXR) can be used in a fast manner. However, the diagnostic performance via CXR needs to be improved, since the identifying these findings are highly time-consuming and prone to human error. Therefore, Artificial Intelligence(AI) based tool may be useful to aid the diagnosis of COVID-19 via CXR. In this study, we explored various Deep learning(DL) approach to classify COVID-19, other viral pneumonia and normal. For the original dataset and lung-segmented dataset, the pre-trained AlexNet, SqueezeNet, ResNet18, DenseNet201 were transfer-trained and validated for 3 class - COVID-19, viral pneumonia, normal. In the results, AlexNet showed the highest mean accuracy of 99.15±2.69% and fastest training time of 1.61±0.56 min among 4 pre-trained neural networks. In this study, we demonstrated the performance of 4 pre-trained neural networks in COVID-19 diagnosis with CXR images. Further, we plotted the class activation map(CAM) of each network and demonstrated that the lung-segmentation pre-processing improve the performance of COVID-19 classifier with CXR images by excluding background features.

• 대한영상의학회지
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• 제81권2호
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• pp.351-364
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• 2020

흉부 X선은 폐와 종격동 질환을 평가하는 데 있어 매우 중요한 일차 영상 검사이다. 초기 흉부 X선에서 놓친 폐암은 환자의 진단을 지연시키고 예후에 중요한 영향을 줄 수 있다. 저자들은 초기 흉부 X선에서 폐암의 중요한 진단적 오류를 피하기 위하여 비교적 흔히 접하게 되는 영상 진단의 함정에 대하여 다양한 증례를 통하여 검토하고 또한 폐암의 다양한 영상 소견의 중요성에 대하여 중점적으로 살펴보고자 한다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제63차 동계학술대회논문집 29권1호
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• pp.31-32
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• 2021

신종 코로나바이러스 감염증(Coronavirus disease 2019; COVID-19)이 빠르게 확산됨에 따라 세계적인 전염병 대유행인 팬데믹(Pandemic)으로 선언되었다. 감염자들은 꾸준히 증가하고 있고 최근에는, 무증상 감염자들이 나타나고 있어 의심 환자를 조기에 판단하고 선별할 수 있는 기술이 필요하다. 본 논문에서는 흉부 방사선 검사(chest Radiography; CXR) 영상을 딥러닝(Deep Learning)하여 정상인, 폐렴 환자, 코로나바이러스 감염자를 분류할 수 있도록 한다.

• Korean Journal of Radiology
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• 제23권10호
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• pp.1009-1018
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• 2022

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

• 대한방사선기술학회지:방사선기술과학
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• 제30권4호
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• pp.335-341
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• 2007

Simple chest radiography에서 정상인의 가로막(diaphragm)에 대한 계측치는 다음과 같다. 1. 전체 대상자에 대한 흉곽(internal diameter of thorax: ID)의 평균은 293.3 mm이었으며, 최소 221.0 mm, 최대 335.3 mm이었다. 2. 가로막의 높이는 오른 가로막이 높은 경우가 81.4%, 오른 가로막과 왼 가로막이 동일한 경우가 16.2%, 왼 가로막이 높은 경우가 2.4% 순으로 나타났다. 3. 오른 가로막이 높은 경우 오른 가로막의 평균 높이는 15.2 mm이었으며, 가장 낮은 경우는 2.0 mm, 가장 높은 경우는 41.7 mm이었다. 4. 왼 가로막이 높은 경우 왼 가로막의 평균 높이는 11.5 mm이었으며, 가장 낮은 경우는 4.7 mm, 가장 높은 경우는 30.4 mm이었다. 5. 가로막의 만곡도에서 오른 가로막의 평균 만곡은 22.9 mm이었고, 가장 작은 경우는 10.4 mm, 가장 큰 경우는 37.3 mm이었다. 6. 왼 가로막의 평균 만곡은 22.4 mm이었고, 가장 작은 경우는 11.3 mm, 가장 큰 경우는 42.2 mm이었다. 7. ID와 오른 가로막과 왼 가로막 만곡에 대한 관계에서 ID는 오른 가로막 만곡(r= .427, p<.001)과 왼 가로막 만곡(r= .425, p<.001)에서 모두 통계적으로 유의미한 정적 상관관계를 보였다. 8. 오른 가로막 만곡과 왼 가로막 만곡의 관계는(r= .403, p<.001) 통계적으로 유의미한 정적 상관관계를 보였다.

• 대한방사선기술학회지:방사선기술과학
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• 제30권2호
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• pp.95-104
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• 2007

특별한 임상적 증상이 없는 대상자 총 1,669명의 단순 흉부방사선영상(simple chest radiography, CXR) 소견을 분석한 결과 다음과 같은 결론을 얻었다. 1. 연구대상자의 일반적 특성은 총 1,669명 중 남자 55.2%, 여자 44.8%이었다. 2. 흉부 질환이 있는 경우는 총 1,669명 중 14.9%인 249명이었다. 3. 연령에 따른 질환의 분석에서는 35세 미만 6.1%, $35{\sim}39$세 9.7%, $40{\sim}49$세 13.3%, 50세 이상 30.8%로 연령이 많을수록 질병이 많았다. 4. 질환의 발생부위는 폐질환만을 고려할 때 right upper lobe가 가장 많았고, both upper lobe, left upper lobe의 순이었다. 5. 질환의 종류는 유소견자 249명 중 pulmonary nodule를 가지고 있는 경우가 55.0%(총 1,669명의 대상자에서 유소견자 249명 중 137명으로 전체 대상자를 기준으로 할 경우 8.2%)로 가장 많았으며, 다음으로 cardiomegaly 24.5%, CP angle blunting 4.8%, scoliosis 4.6%, tortous aorta 2.8%, bronchial luminal dilatation 2.4%, pleural thickening 2.0% 순으로 나타났고, dextrocardia와 cystic dilation of bronchus, cavitary lesion, lung collapse 등은 각각 0.4%로 매우 적었다. 6. 성별에 따른 질환의 종류는 남자가 여자보다 pulmonary nodule이 많았고, 여자는 남자보다 cardiomegaly와 tortous aorta, scoliosis가 더 많았다. 7. 연령에 따른 질환의 종류는 35세 미만이 다른 연령대보다 scoliosis가 많았고, $40{\sim}49$세는 CP angle blunting, $35{\sim}39$세는 pulmonary nodule, 50세 이상은 cardiomegaly와 tortous aorta가 많았다.

• Korean Journal of Radiology
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• 제24권3호
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• pp.259-270
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• 2023

Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.