• Korean Journal of Radiology
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• 제24권8호
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• pp.752-760
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• 2023

Radiologists and trauma surgeons should monitor for early killers among patients with thoracic trauma, such as tension pneumothorax, tracheobronchial injuries, flail chest, aortic injury, mediastinal hematomas, and severe pulmonary parenchymal injury. With the advent of cutting-edge technology, rapid volumetric computed tomography of the chest has become the most definitive diagnostic tool for establishing or excluding thoracic trauma. With the notion of "time is life" at emergency settings, radiologists must find ways to shorten the turnaround time of reports. One way to interpret chest findings is to use a systemic approach, as advocated in this study. Our interpretation of chest findings for thoracic trauma follows the acronym "ABC-Please" in which "A" stands for abnormal air, "B" stands for abnormal bones, "C" stands for abnormal cardiovascular system, and "P" in "Please" stands for abnormal pulmonary parenchyma and vessels. In the future, utilizing an artificial intelligence software can be an alternative, which can highlight significant findings as "warm zones" on the heatmap and can re-prioritize important examinations at the top of the reading list for radiologists to expedite the final reports.

• Korean Journal of Radiology
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• 제23권5호
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• pp.505-516
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• 2022

Objective: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. Materials and Methods: A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated. Results: The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001). Conclusion: AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.

• Korean Journal of Radiology
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• 제23권12호
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

• Korean Journal of Radiology
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• 제21권7호
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• 한국방사선학회논문지
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• 제12권3호
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• pp.417-425
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• 2018

늘어나는 CT 조영제 주입 검사만큼이나 환자의 심리적 불안감은 물론 검사자인 방사선사 또한 조영제 부작용과 혈관 외 유출에 대한 불안감이 커지고 있는 것이 현실이다. 본 연구는 조영증강 CT 검사 시에 무선신호기를 사용하여 조영제 사용에 대한 환자와 방사선사의 불안감과 두려움 등의 심리적 불안해소에 얼마나 도움이 되는지 알아보고자 CT 검사 후에 무선신호기에 대한 환자와 방사선사의 만족도조사를 설문을 통해 실시하였다. 또한 무선신호기를 사용함으로써 CT 검사 시 발생할 수 있는 조영제 부작용에 대한 방사선사의 신속한 대처와 혈관 외 유출 예방에 도움이 되고자 한다. 실험대상은 2017년 8월~11월 4개월간 전남에 소재한 C대학병원을 방문하여 CT를 시행한 20세 이상의 환자를 대상으로 하였고, CT 검사 시행 후 연구에 동의하는 환자 90명을 설문조사하였다. 그 중 남자 57, 여자 33명이다. 방사선사는 CT실에서 근무하며 무선신호기를 사용한 경험이 있는 15명을 대상으로 설문조사하였다. 무선신호기를 사용 전 환자 만족도는 $6.01{\pm}0.88$, 사용 후 환자 만족도는 $8.20{\pm}1.06$으로 사용 전보다 만족도가 높아진 것을 알 수 있었다. 사용 후 방사선사 만족도는 $8.46{\pm}1.06$으로 환자만족도 평균과 큰 차이를 보이지 않았고, 두군 모두 만족도가 8점 이상으로 높게 나타났다. 무선신호기 사용에 따른 심리적 안정 기여도는 과거 부작용 유경험자에서 $8.98{\pm}0.65$, 무경험자 $8.00{\pm}1.21$로 나타났다. 결론적으로 무선신호기의 호출로 방사선사의 신속한 대처를 하는데 도움을 주고 환자와 방사선사에게도 불안감으로부터 심리적 안정을 주어 검사만족도 향상에 도움을 줄 수 있다고 사료된다.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1043-1045
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• 2023

• Korean Journal of Radiology
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• 제24권10호
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• pp.931-932
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• 2023

• Korean Journal of Radiology
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• 제24권10호
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• pp.933-935
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• 2023

• Korean Journal of Radiology
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• 제21권10호
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• pp.1196-1197
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• 2020

• Korean Journal of Radiology
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• 제25권7호
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• pp.595-596
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• 2024