• 재활복지공학회논문지
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• 제10권1호
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• pp.1-7
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• 2016

본 연구는 경산지역 남녀 고등학생을 대상으로 하고, 재검대상자 비율을 통해 청각 선별의 필요성을 고찰하고자 한다. 연구 대상은 여자 고등학생 359명, 남자 고등학생 205명 등 모두 564명으로 하였다. 청각선별은 고막운동성계측(tympanometry)과 자동화 이음향방사(automated otoacoustic emission; AOAE)를 이용하였다. 재검대상자 중 위양성 소견을 보인 여학생 8명(2.228%) 11귀와 남학생 9명(4.39%) 10귀를 제외하면 최종 재검대상자는 여학생이 9명(2.5%), 남학생이 19명(9.268%)으로 각각 관찰되었다. 고막운동도와 AOAE는 남녀 사이 차이가 나타나지 않았다. 결론적으로 청소년 청각선별은 이관 성장 발달에도 불구하고 순음청 각선별로 확인하기 어려운 난청을 선별하기 위하여 고막운동성계측을 시행할 필요가 있고, 위양성 결과를 낮추기 위한 노력이 필요하다.

• Korean Journal of Radiology
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• 제23권12호
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• pp.1290-1300
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• 2022

Objective: "Diagnostic yield," also referred to as the detection rate, is a parameter positioned between diagnostic accuracy and diagnosis-related patient outcomes in research studies that assess diagnostic tests. Unfamiliarity with the term may lead to incorrect usage and delivery of information. Herein, we evaluate the level of proper use of the term "diagnostic yield" and its related parameters in articles published in Radiology and Korean Journal of Radiology (KJR). Materials and Methods: Potentially relevant articles published since 2012 in these journals were identified using MEDLINE and PubMed Central databases. The initial search yielded 239 articles. We evaluated whether the correct definition and study setting of "diagnostic yield" or "detection rate" were used and whether the articles also reported companion parameters for false-positive results. We calculated the proportion of articles that correctly used these parameters and evaluated whether the proportion increased with time (2012-2016 vs. 2017-2022). Results: Among 39 eligible articles (19 from Radiology and 20 from KJR), 17 (43.6%; 11 from Radiology and 6 from KJR) correctly defined "diagnostic yield" or "detection rate." The remaining 22 articles used "diagnostic yield" or "detection rate" with incorrect meanings such as "diagnostic performance" or "sensitivity." The proportion of correctly used diagnostic terms was higher in the studies published in Radiology than in those published in KJR (57.9% vs. 30.0%). The proportion improved with time in Radiology (33.3% vs. 80.0%), whereas no improvement was observed in KJR over time (33.3% vs. 27.3%). The proportion of studies reporting companion parameters was similar between journals (72.7% vs. 66.7%), and no considerable improvement was observed over time. Conclusion: Overall, a minority of articles accurately used "diagnostic yield" or "detection rate." Incorrect usage of the terms was more frequent without improvement over time in KJR than in Radiology. Therefore, improvements are required in the use and reporting of these parameters.

• 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.