1
|
Farič N, Hinder S, Williams R, Ramaesh R, Bernabeu MO, van Beek E, Cresswell K. Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study. J Am Med Inform Assoc 2023; 31:24-34. [PMID: 37748456 PMCID: PMC10746311 DOI: 10.1093/jamia/ocad191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/23/2023] [Accepted: 09/13/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVES Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. MATERIALS AND METHODS We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework. RESULTS We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. DISCUSSION Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. CONCLUSION The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.
Collapse
Affiliation(s)
- Nuša Farič
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Sue Hinder
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Robin Williams
- Institute for the Study of Science, Technology and Innovation, University of Edinburgh, Edinburgh, United Kingdom
| | - Rishi Ramaesh
- Department of Radiology, Royal Infirmary Hospital, Edinburgh, United Kingdom
| | - Miguel O Bernabeu
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- The Bayes Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Edwin van Beek
- Centre for Cardiovascular Science, Edinburgh Imaging and Neuroscience, University of Edinburgh, Edinburgh, United Kingdom
| | | |
Collapse
|
2
|
Huang S, Yang J, Shen N, Xu Q, Zhao Q. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 2023; 89:30-37. [PMID: 36682439 DOI: 10.1016/j.semcancer.2023.01.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.
Collapse
Affiliation(s)
- Shigao Huang
- Department of Radiation Oncology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shanxi, China
| | - Jie Yang
- Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Na Shen
- Hong Kong Shue Yan University, Hong Kong, China
| | - Qingsong Xu
- Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macau SAR, China.
| |
Collapse
|
3
|
Xiong S, Hu H, Liu S, Huang Y, Cheng J, Wan B. Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:265-276. [PMID: 36806541 DOI: 10.3233/xst-221343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
OBJECTIVE To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department. METHODS Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3. RESULTS The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p < 0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p > 0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p < 0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p > 0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p > 0.05). CONCLUSIONS DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.
Collapse
Affiliation(s)
- Shan Xiong
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Hai Hu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Sibin Liu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Yuanyi Huang
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China
| | - Jianmin Cheng
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bing Wan
- Department of Radiology, Renhe Hospital Affiliated to Three Gorges University, Yichang, China
| |
Collapse
|
4
|
Gupta RV, Kalra MK, Ebrahimian S, Kaviani P, Primak A, Bizzo B, Dreyer KJ. Complex Relationship Between Artificial Intelligence and CT Radiation Dose. Acad Radiol 2022; 29:1709-1719. [PMID: 34836775 DOI: 10.1016/j.acra.2021.10.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/15/2021] [Accepted: 10/17/2021] [Indexed: 12/22/2022]
Abstract
Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.
Collapse
Affiliation(s)
- Reya V Gupta
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts.
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Parisa Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Andrew Primak
- Siemens Medical Solutions USA Inc, Malvern, Pennsylvania
| | - Bernardo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
| | - Keith J Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
| |
Collapse
|
5
|
Fang R, Liao H, Mardani A. How to aggregate uncertain and incomplete cognitive evaluation information in lung cancer treatment plan selection? A method based on Dempster-Shafer theory. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.04.060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
6
|
Diao K, Chen Y, Liu Y, Chen BJ, Li WJ, Zhang L, Qu YL, Zhang T, Zhang Y, Wu M, Li K, Song B. Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:668. [PMID: 35845492 PMCID: PMC9279799 DOI: 10.21037/atm-22-2157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
Background Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers’ final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2–98.0%] and a positive predictive value of 55.6% (95% CI: 49.0–62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist’s decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9–732.5) vs. 141.3 (79.3–380.8) mm3, P<0.001], lower average CT number [−511.0 (−576.5 to −100.5) vs. −191.5 (−487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.
Collapse
Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bo-Jiang Chen
- Department of Respiratory Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Wan-Jiang Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Lin Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ya-Li Qu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Sanya People's Hospital (West China Sanya Hospital of Sichuan University), Chengdu, China
| |
Collapse
|
7
|
Zhan X, Long H, Gou F, Duan X, Kong G, Wu J. A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer. SENSORS 2021; 21:s21237996. [PMID: 34884000 PMCID: PMC8659811 DOI: 10.3390/s21237996] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 12/15/2022]
Abstract
In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.
Collapse
Affiliation(s)
- Xiangbing Zhan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Huiyun Long
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
- Correspondence: (H.L.); (J.W.)
| | - Fangfang Gou
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
| | - Xun Duan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Guangqian Kong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Jia Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
- Research Center for Artificial Intelligence, Monash University, Clayton, VIC 3800, Australia
- Correspondence: (H.L.); (J.W.)
| |
Collapse
|
8
|
Zeng D, Wang C, Mu C, Su M, Mao J, Huang J, Xu J, Shao L, Li B, Li H, Li B, Zhao J, Jiang J. Cell-free DNA from bronchoalveolar lavage fluid (BALF): a new liquid biopsy medium for identifying lung cancer. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1080. [PMID: 34422992 PMCID: PMC8339831 DOI: 10.21037/atm-21-2579] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/21/2021] [Indexed: 12/18/2022]
Abstract
Background Differentiating malignant lung tumors from benign pulmonary nodules is a great challenge. While the analysis of bronchoalveolar lavage fluid (BALF) is used for diagnosing infections and interstitial lung diseases, there is limited evidence to support its use for lung cancer diagnosis. This study aimed to interrogate the potential of using BALF cell-free DNA (cfDNA) to discriminate malignant lesions from benign nodules. Methods Fifty-three patients with solid pulmonary nodules (≤2 cm) were prospectively enrolled, including 21 confirmed with benign disease and 32 with malignant tumors. Mutations were profiled for 30 tumor tissues and 40 BALFs. Paired BALFs and plasma from 48 patients underwent DNA methylation profiling. A methylome-based classification model was developed for BALF and plasma separately. Results Among the 30 patients with paired tissues and BALFs, 96.7% and 70% had alterations detected from their tissues (79 alterations) and BALFs (53 alterations), respectively. Using tissues as references, BALFs revealed 14 new alterations and missed 41. BALF mutation displayed a sensitivity of 71%, specificity of 77.8%, and accuracy of 72.5% in detecting lung cancer. BALF methylation achieved an accuracy of 81.3%, with both sensitivity and specificity being 81%. Plasma methylation showed a 66.7% sensitivity, 71.4% specificity, and 68.8% accuracy. BALF methylation also demonstrated 82.4% sensitivity in stage I patients. Parallel bronchoscopy, lavage cytology, and bronchial brushing demonstrated an inferior sensitivity of 23%, 3.1%, and 9.7%, respectively, compared with BALF methylation and mutation (P<0.0001). Conclusions BALF cfDNA can serve as a liquid biopsy media for both mutation and methylation profiling, demonstrating better sensitivities in distinguishing small malignant tumors from benign nodules than conventional methods. Keywords Lung cancer diagnosis; pulmonary nodule; bronchoalveolar lavage fluid (BALF); methylation; genomic mutation
Collapse
Affiliation(s)
- Daxiong Zeng
- Department of Respiratory Medicine, Dusu Lake Hospital to Soochow University, Suzhou, China.,Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Cangguo Wang
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chuanyong Mu
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Meiqin Su
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingyu Mao
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jianan Huang
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiayue Xu
- Burning Rock Biotech, Guangzhou, China
| | - Lin Shao
- Burning Rock Biotech, Guangzhou, China
| | - Bing Li
- Burning Rock Biotech, Guangzhou, China
| | - Haiyan Li
- Burning Rock Biotech, Guangzhou, China
| | - Bingsi Li
- Burning Rock Biotech, Guangzhou, China
| | - Jun Zhao
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Junhong Jiang
- Department of Respiratory Medicine, Dusu Lake Hospital to Soochow University, Suzhou, China.,Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| |
Collapse
|