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Gefter WB, Prokop M, Seo JB, Raoof S, Langlotz CP, Hatabu H. Human-AI Symbiosis: A Path Forward to Improve Chest Radiography and the Role of Radiologists in Patient Care. Radiology 2024; 310:e232778. [PMID: 38259206 PMCID: PMC10831473 DOI: 10.1148/radiol.232778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/08/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Affiliation(s)
- Warren B. Gefter
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Mathias Prokop
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Joon Beom Seo
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Suhail Raoof
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Curtis P. Langlotz
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
| | - Hiroto Hatabu
- From the Department of Radiology, Penn Medicine, University of Pennsylvania, Philadelphia, Pa (W.B.G.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.P.); Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.B.S.); Department of Medicine and Radiology, Zucker School of Medicine, Hofstra/Northwell and Lung Institute, Lenox Hill Hospital, New York, NY (S.R.); Department of Radiology and Biomedical Informatics and Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, Calif (C.P.L.); and Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02215 (H.H.)
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Till T, Tschauner S, Singer G, Lichtenegger K, Till H. Development and optimization of AI algorithms for wrist fracture detection in children using a freely available dataset. Front Pediatr 2023; 11:1291804. [PMID: 38188914 PMCID: PMC10768054 DOI: 10.3389/fped.2023.1291804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction In the field of pediatric trauma computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems have emerged offering a promising avenue for improved patient care. Especially children with wrist fractures may benefit from machine learning (ML) solutions, since some of these lesions may be overlooked on conventional X-ray due to minimal compression without dislocation or mistaken for cartilaginous growth plates. In this article, we describe the development and optimization of AI algorithms for wrist fracture detection in children. Methods A team of IT-specialists, pediatric radiologists and pediatric surgeons used the freely available GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, a total number of 10,643 studies (20,327 images). First, a basic object detection model, a You Only Look Once object detector of the seventh generation (YOLOv7) was trained and tested on these data. Then, team decisions were taken to adjust data preparation, image sizes used for training and testing, and configuration of the detection model. Furthermore, we investigated each of these models using an Explainable Artificial Intelligence (XAI) method called Gradient Class Activation Mapping (Grad-CAM). This method visualizes where a model directs its attention to before classifying and regressing a certain class through saliency maps. Results Mean average precision (mAP) improved when applying optimizations pre-processing the dataset images (maximum increases of + 25.51% mAP@0.5 and + 39.78% mAP@[0.5:0.95]), as well as the object detection model itself (maximum increases of + 13.36% mAP@0.5 and + 27.01% mAP@[0.5:0.95]). Generally, when analyzing the resulting models using XAI methods, higher scoring model variations in terms of mAP paid more attention to broader regions of the image, prioritizing detection accuracy over precision compared to the less accurate models. Discussion This paper supports the implementation of ML solutions for pediatric trauma care. Optimization of a large X-ray dataset and the YOLOv7 model improve the model's ability to detect objects and provide valid diagnostic support to health care specialists. Such optimization protocols must be understood and advocated, before comparing ML performances against health care specialists.
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Affiliation(s)
- Tristan Till
- Department of Applied Computer Sciences, FH JOANNEUM - University of Applied Sciences, Graz, Austria
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Sebastian Tschauner
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Georg Singer
- Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
| | - Klaus Lichtenegger
- Department of Applied Computer Sciences, FH JOANNEUM - University of Applied Sciences, Graz, Austria
| | - Holger Till
- Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
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