인공지능 기반 흉부 후전방향 검사에서 자세 평가 방법에 관한 연구
Study of Posture Evaluation Method in Chest PA Examination based on Artificial Intelligence
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture esti- mation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.
Abstract
Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture esti- mation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.
참고문헌 (40)
- Kelly B, "The chest radiograph", The Ulster medical journal, 81(3), pp.143~ (2012)
- 안병주, "흉부 엑스선 촬영 시 촬영기법에 따른 영상 평가", 한국방사선학회논문지, 16(3), pp.217~223 (2022), 한국방사선학회[DOI]
- Kostat, "2021년 사망원인통계 결과", ~ (2022)
- 보건의료인력 중·장기수급추계연구, "Oh YH", 한국보건사회연구원, ~ (2015)
- Lee SS, "코로나19 방역 현장 방사선사들, 과로, 감염, 피폭 3중고", RAPPORTIAN, ~ (2023)
- 조평곤, "일반엑스선 검사 시 위치 잡이의 중요성: 흉부엑스선 검사 중심으로", 방사선기술과학, 45(3), pp.249~254 (2022), 대한방사선과학회
- 국가인권위원회 발간자료, "2013", ~
- Grasselli G, "Hospital-acquired infections in critically ill patients with COVID-19", Chest, 160(2), pp.454~465 (2021)
- Dong Ok Choi, "코로나 바이러스 감염증-19의 재조합 S1 RBD 단백질을 이용한 COVID-19 바이러스의 중화항체 검사 키트의 개발", 대한임상검사과학회지, 53(3), pp.257~265 (2021), 대한임상검사과학회[DOI]
- Kim DY, "코로나 영웅 '슬픈 기록'…의료진 8076명 감염, 15명 숨졌다", JoongAng, ~ (2022)
- 김지영, "비대면 진료 및 치료를 위한 UX 디자인 기반 인공지능 헬스케어 로봇 시스템 개발 연구 -비대면 진료의 UX 디자인 개발을 중심으로-", 한국디자인문화학회지, 27(4), pp.141~152 (2021), 한국디자인문화학회
- 의료 인공지능 표준화 동향, "Jun JH", 한국전자통신연구원, 113~126 (2019)
- 한지예, "CNN기반의 학습모델을 활용한 거북목 증후군 자세 교정 시스템", 한국콘텐츠학회 논문지, 20(7), pp.47~55 (2020), 한국콘텐츠학회[DOI]
- 김진엽, "인공지능을 활용한 C-Arm에서 수술용 거즈 검출을 위한 데이터셋 구축 및 검출모델 적용에 관한 연구", 의공학회지, 43(4), pp.290~297 (2022), 대한의용생체공학회
- 최종환, "인공지능 딥러닝 알고리즘을 적용한 무용 동작 정량화 연구", 한국무용학회지, 22(1), pp.43~52 (2022), 한국무용학회[DOI]
- 조재민, "비주얼 서보잉을 위한 딥러닝 기반 물체 인식 및 자세 추정", 로봇학회 논문지, 14(1), pp.1~7 (2019), 한국로봇학회[DOI]
- Moon G, "Camera distance-aware topdown approach for 3d multi-person pose estimation from a single rgb image", Proceedings of the IEEE, 2019, 10133~10142
- Ning G, "A top-down approach to articulated human pose estimation and tracking", Proceedings of the European Conference on Computer Vision(ECCV)Workshops (2018)
- Geng Z, "Bottom-up human pose estimation via disentangled keypoint regression", Proceedings of the IEEE, 2021, 14676~14686
- Fang H-S, "IEEE Transactions on Pattern Analysis and Machine Intelligence" (2022)
- Sengupta A, "mm-Pose : Real-time human skeletal posture estimation using mmWave radars and CNNs", IEEE Sensors Journal, 20(17), pp.10032~10044 (2020)
- Wang J, "Cnn-rnn:A unified framework for multi-label image classification", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2285~2294 (2016)
- Targ S, "Resnet in resnet : Generalizing residual architectures", ~ (2016)
- Zhu Y, "Densenet for dense flow", 2017 IEEE international conference on image processing (ICIP), pp.790~794 (2017)
- Tan M, "Efficientnet: Rethinking model scaling for convolutional neural networks", International conference on machine learning, pp.6105~6114 (2019)
- Simonyan K, "Very deep convolutional networks for large-scale image recognition", ~ (2014)
- Szegedy C, "Rethinking the inception architecture for computer vision", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2818~2826 (2016)
- Chollet F, "Xception: Deep learning with depthwise separable convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1251~1258 (2017)
- Deng X, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem", Information Sciences, 340, pp.250~261 (2016)
- Gulli A, "Deep learning with Keras" (2017), Packt Publishing Ltd
- Radiologic Sciences Staffing and Workplace Survey 2019, "asrt", asrt, ~ (2019)
- 주영철, "디지털방사선 환경에서 흉부 후 - 전 방향 검사 시 초점과 영상수용체간 거리의 적절성", 방사선기술과학, 39(2), pp.135~142 (2016), 대한방사선과학회[DOI]
- 조지환, "건강검진 차량 내 흉부 방사선검사 시 공간산란선 분포 연구", 방사선기술과학, 40(3), pp.377~383 (2017), 대한방사선과학회[DOI]
- Coe RP, "Automatic position control system for x-ray machines", Google Patents, ~ (1996)
- LoVette D, "X-ray Automation: Auto Tracking Vs. Auto Positioning" (2019), blockimaging
- Chu WC-C, "Artificial intelligence of things in sports science : weight training as an example", Computer, 52(11), pp.52~61 (2019)
- Novatchkov H, "Artificial intelligence in sports on the example of weight training", Journal of sports science &medicine, 12(1), pp.27~ (2013)
- Creswell A, "Generative adversarial networks : An overview", IEEE signal processing magazine, 35(1), pp.53~65 (2018)