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Elhakim MT, Stougaard SW, Graumann O, Nielsen M, Gerke O, Larsen LB, Rasmussen BSB. AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study. Radiol Artif Intell 2024; 6:e230529. [PMID: 39230423 DOI: 10.1148/ryai.230529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AIfirst), the second reader (scenario 2: integrated AIsecond), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AItriage). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AIfirst showed no evidence of a difference in accuracy metrics except for a higher arbitration rate (+0.99%, P < .001). Integrated AIsecond had lower sensitivity (-1.58%, P < .001), negative predictive value (NPV) (-0.01%, P < .001), and recall rate (-0.06%, P = .04) but a higher positive predictive value (PPV) (+0.03%, P < .001) and arbitration rate (+1.22%, P < .001). Integrated AItriage achieved higher sensitivity (+1.33%, P < .001), PPV (+0.36%, P = .03), and NPV (+0.01%, P < .001) but lower arbitration rate (-0.88%, P < .001). Replacing one or both readers with AI seems feasible; however, the site of application in the workflow can have clinically relevant effects on accuracy and workload. Keywords: Mammography, Breast, Neoplasms-Primary, Screening, Epidemiology, Diagnosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Mohammad T Elhakim
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
| | - Sarah W Stougaard
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
| | - Ole Graumann
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
| | - Mads Nielsen
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
| | - Oke Gerke
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
| | - Lisbet B Larsen
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
| | - Benjamin S B Rasmussen
- From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.)
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Sourlos N, Vliegenthart R, Santinha J, Klontzas ME, Cuocolo R, Huisman M, van Ooijen P. Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology. Insights Imaging 2024; 15:248. [PMID: 39400639 DOI: 10.1186/s13244-024-01833-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 09/20/2024] [Indexed: 10/15/2024] Open
Abstract
Various healthcare domains have witnessed successful preliminary implementation of artificial intelligence (AI) solutions, including radiology, though limited generalizability hinders their widespread adoption. Currently, most research groups and industry have limited access to the data needed for external validation studies. The creation and accessibility of benchmark datasets to validate such solutions represents a critical step towards generalizability, for which an array of aspects ranging from preprocessing to regulatory issues and biostatistical principles come into play. In this article, the authors provide recommendations for the creation of benchmark datasets in radiology, explain current limitations in this realm, and explore potential new approaches. CLINICAL RELEVANCE STATEMENT: Benchmark datasets, facilitating validation of AI software performance can contribute to the adoption of AI in clinical practice. KEY POINTS: Benchmark datasets are essential for the validation of AI software performance. Factors like image quality and representativeness of cases should be considered. Benchmark datasets can help adoption by increasing the trustworthiness and robustness of AI.
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Affiliation(s)
- Nikos Sourlos
- Department of Radiology, University Medical Center of Groningen, Groningen, The Netherlands
- DataScience Center in Health, University Medical Center Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center of Groningen, Groningen, The Netherlands
- DataScience Center in Health, University Medical Center Groningen, Groningen, The Netherlands
| | - Joao Santinha
- Digital Surgery LAB, Champalimaud Foundation, Champalimaud Clinical Centre, Lisbon, Portugal
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter van Ooijen
- DataScience Center in Health, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands.
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Hovda T, Sagstad S, Moshina N, Vigeland E, Hofvind S. Initial interpretation scores of screening mammograms and cancer detection in BreastScreen Norway. Eur J Radiol 2024; 179:111662. [PMID: 39159548 DOI: 10.1016/j.ejrad.2024.111662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/10/2024] [Accepted: 07/31/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE To explore the association between radiologists' interpretation scores, early performance measures and cumulative reading volume in mammographic screening. METHOD We analyzed 1,689,731 screening examinations (3,379,462 breasts) from BreastScreen Norway 2012-2020, all breasts scored 1-5 by two independent radiologists. Score 1 was considered negative/benign and score ≥2 positive in this scoring system. We performed descriptive analyses of recall, screen-detected cancer, positive predictive value (PPV) 1, mammographic features and histopathological characteristics by breast-based interpretation scores, and cumulative reading volume by examination-based interpretation scores. RESULTS Counting breasts and not women, 3.9 % (132,570/3,379,462) had a score of ≥2 by one or both radiologists. Of these, 84.8 % (112,440/132,570) were given a maximum score 2. Total recall rate was 1.6 % (53,735/3,379,462), 69.3 % (37,220/53,735) given maximum score 2. Among the 0.3 % (9733/3,379,462) diagnosed with screen-detected cancer, 34.6 % (3369/9733) had maximum score 3. The percentages of recall, screen-detected cancer and PPV-1 increased by increasing the sum of scores assigned by two radiologists (p < 0.001 for trend). Higher proportions of masses were observed among recalls and screen-detected cancers with low scores, and higher proportions of spiculated masses were observed for high scores (p < 0.001). Proportions of invasive carcinoma, histological grade 3 and lymph node positive tumors were higher for high versus low scores (p < 0.001). The proportion of examinations scored 1 increased by cumulative reading volume. CONCLUSIONS We observed higher rates of recall and screen-detected cancer and less favorable histopathological tumor characteristics for high versus low interpretation scores. However, a considerable number of recalls and screen-detected cancers had low interpretation scores.
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Affiliation(s)
- Tone Hovda
- Department of Radiology, Vestre Viken Hospital Trust, PO Box 800, 3004 Drammen, Norway.
| | - Silje Sagstad
- Section for Breast Cancer Screening, Cancer Registry of Norway, The Norwegian Institute of Public Health, Oslo, Norway
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, The Norwegian Institute of Public Health, Oslo, Norway
| | - Einar Vigeland
- Department of Radiology, Vestfold Hospital, Tønsberg, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, The Norwegian Institute of Public Health, Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway
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Bitencourt AGV. The impact of AI implementation in mammographic screening: redefining dense breast screening practices. Eur Radiol 2024; 34:6296-6297. [PMID: 38662101 DOI: 10.1007/s00330-024-10761-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 03/15/2024] [Accepted: 03/23/2024] [Indexed: 04/26/2024]
Affiliation(s)
- Almir G V Bitencourt
- Department of Imaging, A.C.Camargo Cancer Center, São Paulo, Brazil.
- DASA, São Paulo, Brazil.
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Bergan MB, Larsen M, Moshina N, Bartsch H, Koch HW, Aase HS, Satybaldinov Z, Haldorsen IHS, Lee CI, Hofvind S. AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway. Eur Radiol 2024; 34:6298-6308. [PMID: 38528136 PMCID: PMC11399294 DOI: 10.1007/s00330-024-10681-z] [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: 09/25/2023] [Revised: 01/19/2024] [Accepted: 02/10/2024] [Indexed: 03/27/2024]
Abstract
OBJECTIVE To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.
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Affiliation(s)
- Marie Burns Bergan
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Hauke Bartsch
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | | | - Zhanbolat Satybaldinov
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Ingfrid Helene Salvesen Haldorsen
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
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Gjesvik J, Moshina N, Lee CI, Miglioretti DL, Hofvind S. Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection. JAMA Netw Open 2024; 7:e2437402. [PMID: 39361281 PMCID: PMC11450515 DOI: 10.1001/jamanetworkopen.2024.37402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 08/10/2024] [Indexed: 10/05/2024] Open
Abstract
Importance Early breast cancer detection is associated with lower morbidity and mortality. Objective To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the development of future cancer. Design, Setting, and Participants This retrospective cohort study of 116 495 women aged 50 to 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2023) for breast cancer detection and screening data from multiple, consecutive rounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from September 2023 to August 2024. Exposure Artificial intelligence algorithm score indicating suspicion for the presence of breast cancer. The algorithm provided a continuous cancer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mammogram. Main Outcomes and Measures Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative. Results The mean (SD) age at the first study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breast cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) at the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, and 34.0 (33.6) at the third study round. The mean (SD) differences among women who did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (17.4) at the second study round, and 9.3 (17.3) at the third study round. Areas under the receiver operating characteristic curve for the absolute difference were 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0.96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the third study round for interval cancers. Conclusions and Relevance In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breasts developing vs not developing cancer 4 to 6 years before their eventual detection. These findings suggest that commercial AI algorithms developed for breast cancer detection may identify women at high risk of a future breast cancer, offering a pathway for personalized screening approaches that can lead to earlier cancer diagnosis.
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Affiliation(s)
- Jonas Gjesvik
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway
| | - Nataliia Moshina
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
- Fred Hutchinson Cancer Center, Seattle, Washington
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Solveig Hofvind
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway
- Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway
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Morant R, Gräwingholt A, Subelack J, Kuklinski D, Vogel J, Blum M, Eichenberger A, Geissler A. [The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:773-778. [PMID: 39017722 PMCID: PMC11422457 DOI: 10.1007/s00117-024-01345-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/18/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Mammography screening programs (MSP) have shown that breast cancer can be detected at an earlier stage enabling less invasive treatment and leading to a better survival rate. The considerable numbers of interval breast cancer (IBC) and the additional examinations required, the majority of which turn out not to be cancer, are critically assessed. OBJECTIVE In recent years companies and universities have used machine learning (ML) to develop powerful algorithms that demonstrate astonishing abilities to read mammograms. Can such algorithms be used to improve the quality of MSP? METHOD The original screening mammographies of 251 cases with IBC were retrospectively analyzed using the software ProFound AI® (iCAD) and the results were compared (case score, risk score) with a control group. The relevant current literature was also studied. RESULTS The distributions of the case scores and the risk scores were markedly shifted to higher risks compared to the control group, comparable to the results of other studies. CONCLUSION Retrospective studies as well as our own data show that artificial intelligence (AI) could change our approach to MSP in the future in the direction of personalized screening and could enable a significant reduction in the workload of radiologists, fewer additional examinations and a reduced number of IBCs; however, the results of prospective studies are needed before implementation.
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Affiliation(s)
- R Morant
- Krebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz
| | - A Gräwingholt
- Radiologie am Theater, 33098, Paderborn, Deutschland
| | - J Subelack
- School of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz
| | - D Kuklinski
- School of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz.
| | - J Vogel
- School of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz
| | - M Blum
- Krebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz
| | - A Eichenberger
- Krebsliga Ostschweiz, Flurhofstrasse 7, 9000, St. Gallen, Schweiz
| | - A Geissler
- School of Medicine, Lehrstuhl für Gesundheitsökonomie, -Politik und -Management, Universität St. Gallen, 9000, St. Gallen, Schweiz
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8
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Kassis I, Lederman D, Ben-Arie G, Giladi Rosenthal M, Shelef I, Zigel Y. Detection of breast cancer in digital breast tomosynthesis with vision transformers. Sci Rep 2024; 14:22149. [PMID: 39333178 PMCID: PMC11436893 DOI: 10.1038/s41598-024-72707-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 09/10/2024] [Indexed: 09/29/2024] Open
Abstract
Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 × 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.
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Affiliation(s)
- Idan Kassis
- Department of Biomedical Engineering, Ben Gurion University of the Negev, Be'er-Sheva, 8410501, Israel.
| | - Dror Lederman
- Faculty of Engineering, Holon Institute of Technology, Holon, 5810201, Israel
| | - Gal Ben-Arie
- Imaging Institute, Soroka Medical Center, Be'er-Sheva, 84101, Israel
| | | | - Ilan Shelef
- Imaging Institute, Soroka Medical Center, Be'er-Sheva, 84101, Israel
| | - Yaniv Zigel
- Department of Biomedical Engineering, Ben Gurion University of the Negev, Be'er-Sheva, 8410501, Israel
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9
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Sekine C, Horiguchi J. Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence. Int J Clin Oncol 2024:10.1007/s10147-024-02594-0. [PMID: 39297908 DOI: 10.1007/s10147-024-02594-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 07/16/2024] [Indexed: 09/21/2024]
Abstract
Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects.
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Affiliation(s)
- Chikako Sekine
- Department of Breast Surgery, International University of Health and Welfare, Narita Hospital, 852 Hatakeda Narita, Chiba, 286-0124, Japan.
| | - Jun Horiguchi
- Department of Breast Surgery, International University of Health and Welfare, Narita Hospital, 852 Hatakeda Narita, Chiba, 286-0124, Japan
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10
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Hill H, Roadevin C, Duffy S, Mandrik O, Brentnall A. Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening. JAMA Netw Open 2024; 7:e2431715. [PMID: 39235813 PMCID: PMC11377997 DOI: 10.1001/jamanetworkopen.2024.31715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Importance Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) risk prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains. Objective To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program. Design, Setting, and Participants This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to estimate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 years at screening. Exposures Mammography screening at 1 to 6 yearly screening intervals based on breast cancer risk and standard care (screening every 3 years). Main Outcomes and Measures Incremental net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28). Results Artificial intelligence-based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screening program. A screening schedule of every 6 years for lowest-risk individuals, biannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (US $1.28) was estimated to generate a yearly monetary benefit of approximately £10.6 (US $13.6) million. Conclusions and Relevance In this decision analytical model study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced costs. These results are particularly relevant for health care settings where resources are under pressure. New studies to prospectively evaluate AI-guided screening appear warranted.
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Affiliation(s)
- Harry Hill
- School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
| | - Cristina Roadevin
- Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, United Kingdom
| | - Stephen Duffy
- Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
| | - Olena Mandrik
- School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
| | - Adam Brentnall
- Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
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11
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Park SH, Han K, Lee JG. Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01886-9. [PMID: 39225919 DOI: 10.1007/s11547-024-01886-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024]
Abstract
Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a challenge for clinical radiologists. This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians. While we briefly discuss performance metrics for AI models in binary classification, detection, or segmentation tasks, our primary focus is on less frequently addressed topics in published literature. These include metrics and measures for evaluating multiclass classification; those for evaluating generative AI models, such as models used in image generation or modification and large language models; and outcome measures beyond performance metrics, including patient-centered outcome measures. Our explanations aim to guide clinicians in the appropriate use of these metrics and measures.
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Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea
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12
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Seker ME, Koyluoglu YO, Ozaydin AN, Gurdal SO, Ozcinar B, Cabioglu N, Ozmen V, Aribal E. Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program. Eur Radiol 2024; 34:6145-6157. [PMID: 38388718 PMCID: PMC11364680 DOI: 10.1007/s00330-024-10661-3] [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: 11/20/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVES We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. MATERIALS AND METHODS The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. RESULTS The AI software achieved an AUC of 89.6% (86.1-93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. CONCLUSION This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. CLINICAL RELEVANCE STATEMENT The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. KEY POINTS • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading.
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Affiliation(s)
- Mustafa Ege Seker
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey
| | - Yilmaz Onat Koyluoglu
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey
| | | | | | - Beyza Ozcinar
- Istanbul University, School of Medicine, Istanbul, Turkey
| | | | - Vahit Ozmen
- Istanbul University, School of Medicine, Istanbul, Turkey
| | - Erkin Aribal
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey.
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13
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Huisman M, van Ginneken B, Harvey H. The emperor has few clothes: a realistic appraisal of current AI in radiology. Eur Radiol 2024; 34:5873-5875. [PMID: 38451323 DOI: 10.1007/s00330-024-10664-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 03/08/2024]
Affiliation(s)
- Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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Koch HW, Larsen M, Bartsch H, Martiniussen MA, Styr BM, Fagerheim S, Haldorsen IHS, Hofvind S. How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway. Eur Radiol 2024; 34:6158-6167. [PMID: 38396248 PMCID: PMC11364568 DOI: 10.1007/s00330-024-10662-2] [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: 06/27/2023] [Revised: 01/18/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. METHODS In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. RESULTS All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. CONCLUSION AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. CLINICAL RELEVANCE STATEMENT In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. KEY POINTS • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.
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Affiliation(s)
- Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marit Almenning Martiniussen
- Department of Radiology, Østfold Hospital Trust, Kalnes, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | | | - Siri Fagerheim
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
| | - Ingfrid Helene Salvesen Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
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15
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Schöder H. Machine Learning for Automated Interpretation of Fluorodeoxyglucose-Positron Emission Tomography Scans in Lymphoma. J Clin Oncol 2024; 42:2945-2948. [PMID: 38905572 DOI: 10.1200/jco.24.00675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/09/2024] [Accepted: 04/16/2024] [Indexed: 06/23/2024] Open
Affiliation(s)
- Heiko Schöder
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY
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16
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Frazer HML, Peña-Solorzano CA, Kwok CF, Elliott MS, Chen Y, Wang C, Lippey JF, Hopper JL, Brotchie P, Carneiro G, McCarthy DJ. Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer. Nat Commun 2024; 15:7525. [PMID: 39214982 PMCID: PMC11364867 DOI: 10.1038/s41467-024-51725-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 08/14/2024] [Indexed: 09/04/2024] Open
Abstract
Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9-2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6-10.9% reduction in assessments and 48-80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption.
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Affiliation(s)
- Helen M L Frazer
- St Vincent's BreastScreen, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia.
- BreastScreen Victoria, Caulfield, VIC, Australia.
- Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia.
| | - Carlos A Peña-Solorzano
- Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Chun Fung Kwok
- Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Michael S Elliott
- Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Yuanhong Chen
- School of Computer Science, Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Chong Wang
- School of Computer Science, Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Jocelyn F Lippey
- St Vincent's BreastScreen, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- Department of Surgery, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Peter Brotchie
- Department of Radiology, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Gustavo Carneiro
- School of Computer Science, Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
- Centre for Vision, Speech and Signal Processing (CVSSP), The University of Surrey, Surrey, UK
| | - Davis J McCarthy
- Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
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17
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Fransen SJ, Kwee TC, Rouw D, Roest C, van Lohuizen QY, Simonis FFJ, van Leeuwen PJ, Heijmink S, Ongena YP, Haan M, Yakar D. Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI. Eur Radiol 2024:10.1007/s00330-024-11012-y. [PMID: 39143247 DOI: 10.1007/s00330-024-11012-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/16/2024]
Abstract
OBJECTIVES This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses. MATERIALS AND METHODS A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis. RESULTS A total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis. CONCLUSIONS Patients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient's education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability. CLINICAL RELEVANCE STATEMENT Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone. KEY POINTS Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.
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Affiliation(s)
| | - T C Kwee
- University Medical Center Groningen, Groningen, Netherlands
| | - D Rouw
- Martini Hospital, Groningen, Netherlands
| | - C Roest
- University Medical Center Groningen, Groningen, Netherlands
| | | | | | | | - S Heijmink
- Dutch Cancer Institute, Amsterdam, Netherlands
| | - Y P Ongena
- University of Groningen, Groningen, Netherlands
| | - M Haan
- University of Groningen, Groningen, Netherlands
| | - D Yakar
- University Medical Center Groningen, Groningen, Netherlands
- Dutch Cancer Institute, Amsterdam, Netherlands
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18
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Marinovich ML, Lotter W, Waddell A, Houssami N. Simulated arbitration of discordance between radiologists and artificial intelligence interpretation of breast cancer screening mammograms. J Med Screen 2024:9691413241262960. [PMID: 39129395 DOI: 10.1177/09691413241262960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Artificial intelligence (AI) algorithms have been retrospectively evaluated as replacement for one radiologist in screening mammography double-reading; however, methods for resolving discordance between radiologists and AI in the absence of 'real-world' arbitration may underestimate cancer detection rate (CDR) and recall. In 108,970 consecutive screens from a population screening program (BreastScreen WA, Western Australia), 20,120 were radiologist/AI discordant without real-world arbitration. Recall probabilities were randomly assigned for these screens in 1000 simulations. Recall thresholds for screen-detected and interval cancers (sensitivity) and no cancer (false-positive proportion, FPP) were varied to calculate mean CDR and recall rate for the entire cohort. Assuming 100% sensitivity, the maximum CDR was 7.30 per 1000 screens. To achieve >95% probability that the mean CDR exceeded the screening program CDR (6.97 per 1000), interval cancer sensitivities ≥63% (at 100% screen-detected sensitivity) and ≥91% (at 80% screen-detected sensitivity) were required. Mean recall rate was relatively constant across sensitivity assumptions, but varied by FPP. FPP > 6.5% resulted in recall rates that exceeded the program estimate (3.38%). CDR improvements depend on a majority of interval cancers being detected in radiologist/AI discordant screens. Such improvements are likely to increase recall, requiring careful monitoring where AI is deployed for screen-reading.
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Affiliation(s)
- M Luke Marinovich
- The Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council NSW, Sydney, NSW, Australia
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
| | - William Lotter
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Nehmat Houssami
- The Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council NSW, Sydney, NSW, Australia
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
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Illingworth PJ, Venetis C, Gardner DK, Nelson SM, Berntsen J, Larman MG, Agresta F, Ahitan S, Ahlström A, Cattrall F, Cooke S, Demmers K, Gabrielsen A, Hindkjær J, Kelley RL, Knight C, Lee L, Lahoud R, Mangat M, Park H, Price A, Trew G, Troest B, Vincent A, Wennerström S, Zujovic L, Hardarson T. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med 2024:10.1038/s41591-024-03166-5. [PMID: 39122964 DOI: 10.1038/s41591-024-03166-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/29/2024] [Indexed: 08/12/2024]
Abstract
To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference -1.7%; 95% confidence interval -7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161 .
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Affiliation(s)
| | - Christos Venetis
- IVFAustralia, Sydney, New South Wales, Australia
- Unit for Human Reproduction, 1st Dept of Ob/Gyn, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Centre for Big Data Research in Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - David K Gardner
- Melbourne IVF, Melbourne, Victoria, Australia
- School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Scott M Nelson
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
| | | | | | | | | | - Aisling Ahlström
- IVIRMA Global Research Alliance, Livio Gothenburg, Gothenburg, Sweden
| | | | - Simon Cooke
- IVFAustralia, Sydney, New South Wales, Australia
| | - Kristy Demmers
- Queensland Fertility Group, Brisbane, Queensland, Australia
| | | | | | | | | | - Lisa Lee
- Melbourne IVF, Melbourne, Victoria, Australia
| | | | | | - Hannah Park
- Dept of Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | - Geoffrey Trew
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
- Imperial College London, London, UK
| | - Bettina Troest
- The Fertility Unit, Aalborg University Hospital, Aalborg, Denmark
| | - Anna Vincent
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
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20
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Calderaro J, Žigutytė L, Truhn D, Jaffe A, Kather JN. Artificial intelligence in liver cancer - new tools for research and patient management. Nat Rev Gastroenterol Hepatol 2024; 21:585-599. [PMID: 38627537 DOI: 10.1038/s41575-024-00919-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/11/2024] [Indexed: 07/31/2024]
Abstract
Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced rapidly, influencing cancer care. AI systems are already approved for clinical use in some tumour types (for example, colorectal cancer screening). Crucially, research demonstrates that AI can analyse histopathology, radiology and natural language in liver cancer, and can replace manual tasks and access hidden information in routinely available clinical data. However, for liver cancer, few of these applications have translated into large-scale clinical trials or clinically approved products. Here, we advocate for the incorporation of AI in all stages of liver cancer management. We present a taxonomy of AI approaches in liver cancer, highlighting areas with academic and commercial potential, and outline a policy for AI-based liver cancer management, including interdisciplinary training of researchers, clinicians and patients. The potential of AI in liver cancer is immense, but effort is required to ensure that AI can fulfil expectations.
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Affiliation(s)
- Julien Calderaro
- Département de Pathologie, Assistance Publique Hôpitaux de Paris, Groupe Hospitalier Henri Mondor, Créteil, France
- Institut Mondor de Recherche Biomédicale, MINT-HEP Mondor Integrative Hepatology, Université Paris Est Créteil, Créteil, France
| | - Laura Žigutytė
- Else Kroener Fresenius Center for Digital Health (EKFZ), Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Ariel Jaffe
- Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Section of Digestive Diseases, Yale School of Medicine, New Haven, CT, USA
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health (EKFZ), Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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21
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Kavanagh KT, Pontus C, Cormier LE. Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence. J Patient Saf 2024; 20:307-313. [PMID: 38860829 DOI: 10.1097/pts.0000000000001245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
ABSTRACT Currently, the healthcare workplace is one of the most dangerous in the United States. Over a 3-month period in 2022, two nurses were assaulted every hour. Artificial intelligence (AI) has the potential to prevent workplace violence by developing unique patient insights through accessing almost instantly a patient's medical history, past institutional encounters, and possibly even their social media posts. De-escalating dialog can then be formulated, and hot-button topics avoided. AIs can also monitor patients in waiting areas for potential confrontational behavior.Many have concerns implementing AIs in healthcare. AIs are not expected to be 100% accurate, their performance is not compared with a computer but instead measured against humans. However, AIs are outperforming humans in many tasks. They are especially adept at taking standardized examinations, such as Board Exams, the Uniform Bar Exam, and the SAT and Graduate Record Exam. AIs are also performing diagnosis. Initial reports found that newer models have been observed to equal or outperform physicians in diagnostic accuracy and in the conveyance of empathy.In the area of interdiction, AI robots can both navigate and monitor for confrontational and illegal behavior. A human security agent would then be notified to resolve the situation. Our military is fielding autonomous AI robots to counter potential adversaries. For many, this new arms race has grave implications because of the potential of fielding this same security technology in healthcare and other civil settings.The healthcare delivery sector must determine the future roles of AI in relationship to human workers. AIs should only be used to support a human employee. AIs should not be the primary caregiver and a single human should not be monitoring multiple AIs simultaneously. Similar to not being copyrightable, disinformation produced by AIs should not be afforded 'free speech' protections. Any increase in productivity of an AI will equate with a loss of jobs. We need to ask, If all business sectors utilize AIs, will there be enough paid workers for the purchasing of services and products to keep our economy and society a float?
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Affiliation(s)
| | - Christine Pontus
- Health Watch USA, Massachusetts Nurses Association, Canton, Massachusetts
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Pinto Dos Santos D, Tang A, Wald C, Slavotinek J. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA. J Am Coll Radiol 2024; 21:1292-1310. [PMID: 38276923 DOI: 10.1016/j.jacr.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, Alabama; American College of Radiology Data Science Institute, Reston, Virginia
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, California; Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, California
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany; Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts; Tufts University Medical School, Boston, Massachusetts; Commision on Informatics, and Member, Board of Chancellors, American College of Radiology, Virginia
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia
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23
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Weigel S, Hense HW, Weyer-Elberich V, Gerss J, Heindel W. Breast cancer screening with digital breast tomosynthesis: Is independent double reading still required? ROFO-FORTSCHR RONTG 2024; 196:834-842. [PMID: 38295824 DOI: 10.1055/a-2216-1109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Affiliation(s)
- Stefanie Weigel
- Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Münster, Germany
| | - Hans-Werner Hense
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | | | - Joachim Gerss
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Walter Heindel
- Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Münster, Germany
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24
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Cè M, Ibba S, Cellina M, Tancredi C, Fantesini A, Fazzini D, Fortunati A, Perazzo C, Presta R, Montanari R, Forzenigo L, Carrafiello G, Papa S, Alì M. Radiologists' perceptions on AI integration: An in-depth survey study. Eur J Radiol 2024; 177:111590. [PMID: 38959557 DOI: 10.1016/j.ejrad.2024.111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. METHODS A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. RESULTS The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. CONCLUSION Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Chiara Tancredi
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | | | - Deborah Fazzini
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Alice Fortunati
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Chiara Perazzo
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Roberta Presta
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Laura Forzenigo
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging SpA, Via Caduti di Marcinelle, 20134 Milan, Italy.
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25
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Nanaa M, Gupta VO, Hickman SE, Allajbeu I, Payne NR, Arponen O, Black R, Huang Y, Priest AN, Gilbert FJ. Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography. Radiology 2024; 312:e232303. [PMID: 39189901 DOI: 10.1148/radiol.232303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. Purpose To evaluate AI localizations of interval cancers (ICs) on screening mammograms by IC category and histopathologic characteristics. Materials and Methods A screening mammography data set (median patient age, 57 years [IQR, 52-64 years]) that had been assessed by two human readers from January 2011 to December 2018 was retrospectively analyzed using a commercial AI system. The AI outputs were lesion locations (heatmaps) and the highest per-lesion risk score (range, 0-100) assigned to each case. AI heatmaps were considered false positive (FP) if they occurred on normal screening mammograms or on IC screening mammograms (ie, in patients subsequently diagnosed with IC) but outside the cancer boundary. A panel of consultant radiology experts classified ICs as normal or benign (true negative [TN]), uncertain (minimal signs of malignancy [MS]), or suspicious (false negative [FN]). Several specificity and sensitivity thresholds were applied. Mann-Whitney U tests, Kruskal-Wallis tests, and χ2 tests were used to compare groups. Results A total of 2052 screening mammograms (514 ICs and 1548 normal mammograms) were included. The median AI risk score was 50 (IQR, 32-82) for TN ICs, 76 (IQR, 41-90) for ICs with MS, and 89 (IQR, 81-95) for FN ICs (P = .005). Higher median AI scores were observed for invasive tumors (62 [IQR, 39-88]) than for noninvasive tumors (33 [IQR, 20-55]; P < .01) and for high-grade (grade 2-3) tumors (62 [IQR, 40-87]) than for low-grade (grade 0-1) tumors (45 [IQR, 26-81]; P = .02). At the 96% specificity threshold, the AI algorithm flagged 121 of 514 (23.5%) ICs and correctly localized the IC in 93 of 121 (76.9%) cases, with 48 FP heatmaps on the mammograms for ICs (rate, 0.093 per case) and 74 FP heatmaps on normal mammograms (rate, 0.048 per case). The AI algorithm correctly localized a lower proportion of TN ICs (54 of 427; 12.6%) than ICs with MS (35 of 76; 46%) and FN ICs (four of eight; 50% [95% CI: 13, 88]; P < .001). The AI algorithm localized a higher proportion of node-positive than node-negative cancers (P = .03). However, no evidence of a difference by cancer type (P = .09), grade (P = .27), or hormone receptor status (P = .12) was found. At 89.8% specificity and 79% sensitivity thresholds, AI detection increased to 181 (35.2%) and 256 (49.8%) of the 514 ICs, respectively, with FP heatmaps on 158 (10.2%) and 307 (19.8%) of the 1548 normal mammograms. Conclusion Use of a standalone AI system improved early cancer detection by correctly identifying some cancers missed by two human readers, with no differences based on histopathologic features except for node-positive cancers. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Muzna Nanaa
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Vaishnavi O Gupta
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Sarah E Hickman
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Iris Allajbeu
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Nicholas R Payne
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Otso Arponen
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Richard Black
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Yuan Huang
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Andrew N Priest
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
| | - Fiona J Gilbert
- From the Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England (M.N., V.O.G., S.E.H., I.A., N.R.P., O.A., Y.H., A.N.P., F.J.G.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.N., I.A., R.B., A.N.P., F.J.G.); and Department of Radiology, The Royal London Hospital, Barts Health NHS Trust, London, England (S.E.H.)
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Hong N, Whittier DE, Glüer CC, Leslie WD. The potential role for artificial intelligence in fracture risk prediction. Lancet Diabetes Endocrinol 2024; 12:596-600. [PMID: 38942044 DOI: 10.1016/s2213-8587(24)00153-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/30/2024]
Abstract
Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI-ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI-ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI-ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.
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Affiliation(s)
- Namki Hong
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; Institute for Innovation in Digital Healthcare, Yonsei University Health System, Seoul, Korea.
| | - Danielle E Whittier
- McCaig Institute for Bone and Joint Health and Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Claus-C Glüer
- Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - William D Leslie
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
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27
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Djuric O, Deandrea S, Mantellini P, Sardanelli F, Venturelli F, Montemezzi S, Vecchio R, Bucchi L, Senore C, Giordano L, Paci E, Bonifacino A, Calabrese M, Caumo F, Degrassi F, Sassoli De' Bianchi P, Battisti F, Zappa M, Pattacini P, Campari C, Nitrosi A, Di Leo G, Frigerio A, Magni V, Fornasa F, Romanucci G, Falini P, Auzzi N, Armaroli P, Giorgi Rossi P. Organizational impact of systemic implementation of digital breast tomosynthesis as a primary test for breast cancer screening in Italy. LA RADIOLOGIA MEDICA 2024; 129:1156-1172. [PMID: 39042203 DOI: 10.1007/s11547-024-01849-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/04/2024] [Indexed: 07/24/2024]
Abstract
PURPOSE We present a comprehensive investigation into the organizational, social, and ethical impact of implementing digital breast tomosynthesis (DBT) as a primary test for breast cancer screening in Italy. The analyses aimed to assess the feasibility of DBT specifically for all women aged 45-74, women aged 45-49 only, or those with dense breasts only. METHODS Questions were framed according to the European Network of Health Technology Assessment (EuNetHTA) Screening Core Model to produce evidence for the resources, equity, acceptability, and feasibility domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) decision framework. The study integrated evidence from the literature, the MAITA DBT trials, and Italian pilot programs. Structured interviews, surveys, and systematic reviews were conducted to gather data on organizational impact, acceptability among women, reading and acquisition times, and the technical requirements of DBT in screening. RESULTS Implementing DBT could significantly affect the screening program, primarily due to increased reading times and the need for additional human resources (radiologists and radiographers). Participation rates in DBT screening were similar, if not better, to those observed with standard digital mammography, indicating good acceptability among women. The study also highlighted the necessity for specific training for radiographers. The interviewed key persons unanimously considered feasible tailored screening strategies based on breast density or age, but they require effective communication with the target population. CONCLUSIONS An increase in radiologists' and radiographers' workload limits the feasibility of DBT screening. Tailored screening strategies may maximize the benefits of DBT while mitigating potential challenges.
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Affiliation(s)
- Olivera Djuric
- Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Centre for Environmental, Nutritional and Genetic Epidemiology (CREAGEN), University of Modena and Reggio Emilia, Modena, Italy
| | | | - Paola Mantellini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | | | | | | | | | - Lauro Bucchi
- IRCCS Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori"-IRST S.r.l., Meldola, Forlì-Cesena, Italy
| | - Carlo Senore
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
| | - Livia Giordano
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
| | | | | | | | | | - Flori Degrassi
- Associazione Nazionale Donne Operate al Seno-ANDOS, Milan, Italy
| | | | - Francesca Battisti
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Marco Zappa
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | | | - Cinzia Campari
- Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Nitrosi
- Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giovanni Di Leo
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Alfonso Frigerio
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
| | - Veronica Magni
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Francesca Fornasa
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, San Bonifacio, Verona, Italy
| | - Giovanna Romanucci
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, San Bonifacio, Verona, Italy
| | - Patrizia Falini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Noemi Auzzi
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Paola Armaroli
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
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Iacucci M, Santacroce G, Zammarchi I, Maeda Y, Del Amor R, Meseguer P, Kolawole BB, Chaudhari U, Di Sabatino A, Danese S, Mori Y, Grisan E, Naranjo V, Ghosh S. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol 2024; 9:758-772. [PMID: 38759661 DOI: 10.1016/s2468-1253(24)00053-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 05/19/2024]
Abstract
Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Pablo Meseguer
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain; Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | | | | | - Antonio Di Sabatino
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy; First Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele and University Vita-Salute San Raffaele, Milan, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Enrico Grisan
- School of Engineering, London South Bank University, London, UK
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
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Guni A, Whiting P, Darzi A, Ashrafian H. The next generation of evidence synthesis for diagnostic accuracy studies in artificial intelligence. Lancet Digit Health 2024; 6:e541-e542. [PMID: 38926009 DOI: 10.1016/s2589-7500(24)00115-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/22/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024]
Affiliation(s)
- Ahmad Guni
- Institute of Global Health Innovation, Imperial College London, London W2 1NY, UK; Department of Surgery and Cancer, Imperial College London, London, UK
| | - Penny Whiting
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London W2 1NY, UK; Department of Surgery and Cancer, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London W2 1NY, UK; Department of Surgery and Cancer, Imperial College London, London, UK.
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Żydowicz WM, Skokowski J, Marano L, Polom K. Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients. J Clin Med 2024; 13:4337. [PMID: 39124604 PMCID: PMC11313674 DOI: 10.3390/jcm13154337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/22/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.
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Affiliation(s)
- Weronika Magdalena Żydowicz
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
| | - Jaroslaw Skokowski
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Luigi Marano
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Karol Polom
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznan, Poland
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31
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Ellis SG, Kattan MW. Optimizing the Use of Artificial Intelligence in Cardiology in 2024. JACC Cardiovasc Interv 2024; 17:1717-1718. [PMID: 38970582 DOI: 10.1016/j.jcin.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 05/10/2024] [Indexed: 07/08/2024]
Affiliation(s)
- Stephen G Ellis
- Heart, Thoracic and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA.
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
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Rejmer C, Dihge L, Bendahl PO, Förnvik D, Dustler M, Rydén L. Preoperative prediction of nodal status using clinical data and artificial intelligence derived mammogram features enabling abstention of sentinel lymph node biopsy in breast cancer. Front Oncol 2024; 14:1394448. [PMID: 39050572 PMCID: PMC11266164 DOI: 10.3389/fonc.2024.1394448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction Patients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB. Materials and methods This retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated. Results Radiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63-0.72) in internal validation and 0.64 (95% confidence interval: 0.59-0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB. Conclusion This study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla.
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Affiliation(s)
- Cornelia Rejmer
- Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden
| | - Looket Dihge
- Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden
- Department of Plastic and Reconstructive Surgery, Skåne University Hospital, Malmö, Sweden
| | - Pär-Ola Bendahl
- Department of Clinical Sciences, Division of Oncology, Lund University, Lund, Sweden
| | - Daniel Förnvik
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
- Department of Hematology, Oncology and Radiations Physics, Skåne University Hospital, Lund, Sweden
| | - Magnus Dustler
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Lisa Rydén
- Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden
- Department of Surgery, Skåne University Hospital, Malmö, Sweden
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Çelik L, Aribal E. The efficacy of artificial intelligence (AI) in detecting interval cancers in the national screening program of a middle-income country. Clin Radiol 2024; 79:e885-e891. [PMID: 38649312 DOI: 10.1016/j.crad.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/14/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024]
Abstract
AIM We aimed to investigate the efficiency and accuracy of an artificial intelligence (AI) algorithm for detecting interval cancers in a middle-income country's national screening program. MATERIAL AND METHODS A total of 2,129,486 mammograms reported as BIRADS 1 and 2 were matched with the national cancer registry for interval cancers (IC). The IC group consisted of 442 cases, of which 36 were excluded due to having mammograms incompatible with the AI system. A control group of 446 women with two negative consequent mammograms was defined as time-proven normal and constituted the normal group. The cancer risk scores of both groups were determined from 1 to 10 with the AI system. The sensitivity and specificity values of the AI system were defined in terms of IC detection. The IC group was divided into subgroups with six-month intervals according to their time from screening to diagnosis: 0-6 months, 6-12 months, 12-18 months, and 18-24 months. The diagnostic performance of the AI system for all patients was evaluated using receiver operating characteristics (ROC) curve analysis. The diagnostic performance of the AI system for major and minor findings that expert readers determined was re-evaluated. RESULTS AI labeled 53% of ICs with the highest score of 10. The sensitivity of AI in detecting ICs was 53.7% and 38.5% at specificities of 90% and 95%, respectively. Area under the curve (AUC) of AI in detecting major signs was 0.93 (95% CI: 0.90-0.95) with a sensitivity of 81.6% and 72.4% at specificities of 90% and 95%, respectively (95% CI: 0.73-0.88 and 95% CI: 0.60-0.82 respectively) and minor signs was 0.87 (95% CI: 0.87-0.92) with a sensitivity of 70% and 53% at a specificity of 90% and 95%, respectively (95% CI: 0.65-0.82 and 95% CI: 0.52-0.71 respectively). In subgroup analysis for time to diagnosis, the AUC value of the AI system was higher in the 0-6 month period than in later periods. CONCLUSION This study showed the potential of AI in detecting ICs in initial mammograms and reducing human errors and undetected cancers.
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Affiliation(s)
- L Çelik
- Maltepe University Hospital, Feyzullah cad 39, Maltepe, 34843, Istanbul, Turkey.
| | - E Aribal
- Acibadem University, School of Medicine, 34752, Istanbul, Turkey; Acibadem Altunizade Hospital, Tophanelioglu cad 13, Altunizade, 34662, Istanbul, Turkey.
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Saha A, Bosma JS, Twilt JJ, van Ginneken B, Bjartell A, Padhani AR, Bonekamp D, Villeirs G, Salomon G, Giannarini G, Kalpathy-Cramer J, Barentsz J, Maier-Hein KH, Rusu M, Rouvière O, van den Bergh R, Panebianco V, Kasivisvanathan V, Obuchowski NA, Yakar D, Elschot M, Veltman J, Fütterer JJ, de Rooij M, Huisman H. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 2024; 25:879-887. [PMID: 38876123 DOI: 10.1016/s1470-2045(24)00220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. METHODS In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341. FINDINGS Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001). INTERPRETATION An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system. FUNDING Health~Holland and EU Horizon 2020.
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Affiliation(s)
- Anindo Saha
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands; Minimally Invasive Image-Guided Intervention Center, Radboud University Medical Center, Nijmegen, Netherlands.
| | - Joeran S Bosma
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jasper J Twilt
- Minimally Invasive Image-Guided Intervention Center, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anders Bjartell
- Department of Urology, Skåne University Hospital, Malmö, Sweden; Division of Translational Cancer Research, Lund University Cancer Centre, Lund, Sweden
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, London, UK
| | - David Bonekamp
- Division of Radiology, Deutsches Krebsforschungszentrum Heidelberg, Heidelberg, Germany
| | - Geert Villeirs
- Department of Diagnostic Sciences, Ghent University Hospital, Ghent, Belgium
| | - Georg Salomon
- Martini Clinic, Prostate Cancer Center, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Gianluca Giannarini
- Urology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy
| | - Jayashree Kalpathy-Cramer
- Division of Artificial Medical Intelligence in Ophthalmology, University of Colorado, Aurora, CO, USA
| | - Jelle Barentsz
- Department of Medical Imaging, Andros Clinics, Arnhem, Netherlands
| | - Klaus H Maier-Hein
- Division of Medical Image Computing, Deutsches Krebsforschungszentrum Heidelberg, Heidelberg, Germany; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mirabela Rusu
- Departments of Radiology, Urology and Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Faculté de Médecine Lyon-Est, Université de Lyon, Lyon, France
| | | | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Veeru Kasivisvanathan
- Division of Surgery and Interventional Sciences, University College London and University College London Hospital, London, UK
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences and Department of Diagnostic Radiology, Cleveland Clinic Foundation, Cleveland OH, USA
| | - Derya Yakar
- Department of Radiology, University Medical Center Groningen, Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Tronheim, Norway; Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jeroen Veltman
- Department of Radiology, Ziekenhuisgroep Twente, Hengelo, Netherlands; Department of Multi-Modality Medical Imaging, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Jurgen J Fütterer
- Minimally Invasive Image-Guided Intervention Center, Radboud University Medical Center, Nijmegen, Netherlands
| | - Maarten de Rooij
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Tronheim, Norway
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Whitney HM, Yoeli-Bik R, Abramowicz JS, Lan L, Li H, Longman RE, Lengyel E, Giger ML. AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging. J Med Imaging (Bellingham) 2024; 11:044505. [PMID: 39114540 PMCID: PMC11301525 DOI: 10.1117/1.jmi.11.4.044505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/21/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
Purpose Segmentation of ovarian/adnexal masses from surrounding tissue on ultrasound images is a challenging task. The separation of masses into different components may also be important for radiomic feature extraction. Our study aimed to develop an artificial intelligence-based automatic segmentation method for transvaginal ultrasound images that (1) outlines the exterior boundary of adnexal masses and (2) separates internal components. Approach A retrospective ultrasound imaging database of adnexal masses was reviewed for exclusion criteria at the patient, mass, and image levels, with one image per mass. The resulting 54 adnexal masses (36 benign/18 malignant) from 53 patients were separated by patient into training (26 benign/12 malignant) and independent test (10 benign/6 malignant) sets. U-net segmentation performance on test images compared to expert detailed outlines was measured using the Dice similarity coefficient (DSC) and the ratio of the Hausdorff distance to the effective diameter of the outline (R HD - D ) for each mass. Subsequently, in discovery mode, a two-level fuzzy c-means (FCM) unsupervised clustering approach was used to separate the pixels within masses belonging to hypoechoic or hyperechoic components. Results The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], andR HD - D was 0.04 [0.01, 0.12], indicating strong agreement with expert outlines. Clinical review of the internal separation of masses into echogenic components demonstrated a strong association with mass characteristics. Conclusion A combined U-net and FCM algorithm for automatic segmentation of adnexal masses and their internal components achieved excellent results compared with expert outlines and review, supporting future radiomic feature-based classification of the masses by components.
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Affiliation(s)
- Heather M. Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Roni Yoeli-Bik
- The University of Chicago, Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, Chicago, Illinois, United States
| | - Jacques S. Abramowicz
- The University of Chicago, Department of Obstetrics and Gynecology/Section of Ultrasound, Genetics, and Fetal Neonatal Care Center, Chicago, Illinois, United States
| | - Li Lan
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Hui Li
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Ryan E. Longman
- The University of Chicago, Department of Obstetrics and Gynecology/Section of Ultrasound, Genetics, and Fetal Neonatal Care Center, Chicago, Illinois, United States
| | - Ernst Lengyel
- The University of Chicago, Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, Chicago, Illinois, United States
| | - Maryellen L. Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Misera L, Müller-Franzes G, Truhn D, Kather JN. Weakly Supervised Deep Learning in Radiology. Radiology 2024; 312:e232085. [PMID: 39041937 DOI: 10.1148/radiol.232085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard labels, typically applied manually by experts. In contrast, weakly supervised learning is more scalable. Weak supervision comprises situations in which only a portion of the data are labeled (incomplete supervision), labels refer to a whole region or case as opposed to a precisely delineated image region (inexact supervision), or labels contain errors (inaccurate supervision). In many applications, weak labels are sufficient to train useful models. Thus, weakly supervised learning can unlock a large amount of otherwise unusable data for training DL models. One example of this is using large language models to automatically extract weak labels from free-text radiology reports. Here, we outline the key concepts in weakly supervised learning and provide an overview of applications in radiologic image analysis. With more fundamental and clinical translational work, weakly supervised learning could facilitate the uptake of DL in radiology and research workflows by enabling large-scale image analysis and advancing the development of new DL-based biomarkers.
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Affiliation(s)
- Leo Misera
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
| | - Gustav Müller-Franzes
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
| | - Daniel Truhn
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
| | - Jakob Nikolas Kather
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
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Wilkinson LS, Dunbar JK, Lip G. Clinical Integration of Artificial Intelligence for Breast Imaging. Radiol Clin North Am 2024; 62:703-716. [PMID: 38777544 DOI: 10.1016/j.rcl.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
This article describes an approach to planning and implementing artificial intelligence products in a breast screening service. It highlights the importance of an in-depth understanding of the end-to-end workflow and effective project planning by a multidisciplinary team. It discusses the need for monitoring to ensure that performance is stable and meets expectations, as well as focusing on the potential for inadvertantly generating inequality. New cross-discipline roles and expertise will be needed to enhance service delivery.
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Affiliation(s)
- Louise S Wilkinson
- Oxford Breast Imaging Centre, Churchill Hospital, Old Road, Headington, Oxford OX3 7LE, UK.
| | - J Kevin Dunbar
- Regional Head of Screening Quality Assurance Service (SQAS) - South, NHS England, England, UK
| | - Gerald Lip
- North East Scotland Breast Screening Service, Aberdeen Royal Infirmary, Foresterhill Road, Aberdeen AB25 2XF, UK
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Raya-Povedano JL. AI in breast cancer screening: a critical overview of what we know. Eur Radiol 2024; 34:4774-4775. [PMID: 38123690 PMCID: PMC11213721 DOI: 10.1007/s00330-023-10530-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/08/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Affiliation(s)
- José Luis Raya-Povedano
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain.
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofia University Hospital, Menéndez Pidal Avenue s/n, 14004, Córdoba, Spain.
- University of Córdoba, Córdoba, Spain.
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Kahraman G, Haberal KM, Dilek ON. Imaging features and management of focal liver lesions. World J Radiol 2024; 16:139-167. [PMID: 38983841 PMCID: PMC11229941 DOI: 10.4329/wjr.v16.i6.139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/28/2024] [Accepted: 05/22/2024] [Indexed: 06/26/2024] Open
Abstract
Notably, the number of incidentally detected focal liver lesions (FLLs) has increased dramatically in recent years due to the increased use of radiological imaging. The diagnosis of FLLs can be made through a well-documented medical history, physical examination, laboratory tests, and appropriate imaging methods. Although benign FLLs are more common than malignant ones in adults, even in patients with primary malignancy, accurate diagnosis of incidental FLLs is of utmost clinical significance. In clinical practice, FLLs are frequently evaluated non-invasively using ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although US is a cost-effective and widely used imaging method, its diagnostic specificity and sensitivity for FLL characterization are limited. FLLs are primarily characterized by obtaining enhancement patterns through dynamic contrast-enhanced CT and MRI. MRI is a problem-solving method with high specificity and sensitivity, commonly used for the evaluation of FLLs that cannot be characterized by US or CT. Recent technical advancements in MRI, along with the use of hepatobiliary-specific MRI contrast agents, have significantly improved the success of FLL characterization and reduced unnecessary biopsies. The American College of Radiology (ACR) appropriateness criteria are evidence-based recommendations intended to assist clinicians in selecting the optimal imaging or treatment option for their patients. ACR Appropriateness Criteria Liver Lesion-Initial Characterization guideline provides recommendations for the imaging methods that should be used for the characterization of incidentally detected FLLs in various clinical scenarios. The American College of Gastroenterology (ACG) Clinical Guideline offers evidence-based recommendations for both the diagnosis and management of FLL. American Association for the Study of Liver Diseases (AASLD) Practice Guidance provides an approach to the diagnosis and management of patients with hepatocellular carcinoma. In this article, FLLs are reviewed with a comprehensive analysis of ACR Appropriateness Criteria, ACG Clinical Guideline, AASLD Practice Guidance, and current medical literature from peer-reviewed journals. The article includes a discussion of imaging methods used for the assessment of FLL, current recommended imaging techniques, innovations in liver imaging, contrast agents, imaging features of common nonmetastatic benign and malignant FLL, as well as current management recommendations.
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Affiliation(s)
- Gökhan Kahraman
- Department of Radiology, Suluova State Hospital, Amasya 05500, Türkiye
| | - Kemal Murat Haberal
- Department of Radiology, Başkent University Faculty of Medicine, Ankara 06490, Türkiye
| | - Osman Nuri Dilek
- Department of Surgery, İzmir Katip Celebi University, School of Medicine, İzmir 35150, Türkiye
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Lee L, Salami RK, Martin H, Shantharam L, Thomas K, Ashworth E, Allan E, Yung KW, Pauling C, Leyden D, Arthurs OJ, Shelmerdine SC. "How I would like AI used for my imaging": children and young persons' perspectives. Eur Radiol 2024:10.1007/s00330-024-10839-9. [PMID: 38900281 DOI: 10.1007/s00330-024-10839-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/11/2024] [Accepted: 04/27/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) tools are becoming more available in modern healthcare, particularly in radiology, although less attention has been paid to applications for children and young people. In the development of these, it is critical their views are heard. MATERIALS AND METHODS A national, online survey was publicised to UK schools, universities and charity partners encouraging any child or young adult to participate. The survey was "live" for one year (June 2022 to 2023). Questions about views of AI in general, and in specific circumstances (e.g. bone fractures) were asked. RESULTS One hundred and seventy-one eligible responses were received, with a mean age of 19 years (6-23 years) with representation across all 4 UK nations. Most respondents agreed or strongly agreed they wanted to know the accuracy of an AI tool that was being used (122/171, 71.3%), that accuracy was more important than speed (113/171, 66.1%), and that AI should be used with human oversight (110/171, 64.3%). Many respondents (73/171, 42.7%) felt AI would be more accurate at finding problems on bone X-rays than humans, with almost all respondents who had sustained a missed fracture strongly agreeing with that sentiment (12/14, 85.7%). CONCLUSIONS Children and young people in our survey had positive views regarding AI, and felt it should be integrated into modern healthcare, but expressed a preference for a "medical professional in the loop" and accuracy of findings over speed. Key themes regarding information on AI performance and governance were raised and should be considered prior to future AI implementation for paediatric healthcare. CLINICAL RELEVANCE STATEMENT Artificial intelligence (AI) integration into clinical practice must consider all stakeholders, especially paediatric patients who have largely been ignored. Children and young people favour AI involvement with human oversight, seek assurances for safety, accuracy, and clear accountability in case of failures. KEY POINTS Paediatric patient's needs and voices are often overlooked in AI tool design and deployment. Children and young people approved of AI, if paired with human oversight and reliability. Children and young people are stakeholders for developing and deploying AI tools in paediatrics.
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Affiliation(s)
- Lauren Lee
- Young Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, WC1H 3JH, UK
| | | | - Helena Martin
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Kate Thomas
- Royal Hospital for Children & Young People, Edinburgh, Scotland, UK
| | - Emily Ashworth
- St George's Hospital, Blackshaw Road, Tooting London, London, UK
| | - Emma Allan
- Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1H 3JH, UK
| | - Ka-Wai Yung
- Wellcome/ EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK
| | - Cato Pauling
- University College London, Gower Street, London, WC1E 6BT, UK.
| | - Deirdre Leyden
- Young Persons Advisory Group (YPAG), Great Ormond Street Hospital for Children, London, WC1H 3JH, UK
| | - Owen J Arthurs
- Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1H 3JH, UK
- UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK, WC1N 1EH, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, 30 Guilford Street, Bloomsbury, London, WC1N 1EH, UK
| | - Susan Cheng Shelmerdine
- Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1H 3JH, UK
- UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK, WC1N 1EH, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, 30 Guilford Street, Bloomsbury, London, WC1N 1EH, UK
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Reitsam NG, Enke JS, Vu Trung K, Märkl B, Kather JN. Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers. Digestion 2024; 105:331-344. [PMID: 38865982 PMCID: PMC11457979 DOI: 10.1159/000539678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly entering and transforming not only medical research but also clinical practice. In the last 10 years, new AI methods have enabled computers to perform visual tasks, reaching high performance and thereby potentially supporting and even outperforming human experts. This is in particular relevant for colorectal cancer (CRC), which is the 3rd most common cancer type in general, as along the CRC patient journey many complex visual tasks need to be performed: from endoscopy over imaging to histopathology; the screening, diagnosis, and treatment of CRC involve visual image analysis tasks. SUMMARY In all these clinical areas, AI models have shown promising results by supporting physicians, improving accuracy, and providing new biological insights and biomarkers. By predicting prognostic and predictive biomarkers from routine images/slides, AI models could lead to an improved patient stratification for precision oncology approaches in the near future. Moreover, it is conceivable that AI models, in particular together with innovative techniques such as single-cell or spatial profiling, could help identify novel clinically as well as biologically meaningful biomarkers that could pave the way to new therapeutic approaches. KEY MESSAGES Here, we give a comprehensive overview of AI in colorectal cancer, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC.
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Affiliation(s)
- Nic Gabriel Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany,
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany,
| | - Johanna Sophie Enke
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Kien Vu Trung
- Division of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Nilsen P, Sundemo D, Heintz F, Neher M, Nygren J, Svedberg P, Petersson L. Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare. FRONTIERS IN HEALTH SERVICES 2024; 4:1368030. [PMID: 38919828 PMCID: PMC11196845 DOI: 10.3389/frhs.2024.1368030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024]
Abstract
Background Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Challenges with the three components of EBP Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI assistance for the three components of EBP AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. Conclusion This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.
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Affiliation(s)
- Per Nilsen
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - David Sundemo
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Lerum Närhälsan Primary Healthcare Center, Lerum, Sweden
| | - Fredrik Heintz
- Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Margit Neher
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Lena Petersson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Gordin Kopylov L, Goldrat I, Maymon R, Svirsky R, Wiener Y, Klang E. Utilizing ChatGPT to Facilitate Referrals for Fetal Echocardiography. Fetal Diagn Ther 2024; 51:474-477. [PMID: 38834046 DOI: 10.1159/000539658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 05/24/2024] [Indexed: 06/06/2024]
Abstract
INTRODUCTION OpenAI's GPT-4 (artificial intelligence [AI]) is being studied for its use as a medical decision support tool. This research examines its accuracy in refining referrals for fetal echocardiography (FE) to improve early detection and outcomes related to congenital heart defects (CHDs). METHODS Past FE data referred to our institution were evaluated separately by pediatric cardiologist, gynecologist (human experts [experts]), and AI, according to established guidelines. We compared experts and AI's agreement on referral necessity, with experts addressing discrepancies. RESULTS Total of 59 FE cases were addressed retrospectively. Cardiologist, gynecologist, and AI recommended performing FE in 47.5%, 49.2%, and 59.0% of cases, respectively. Comparing AI recommendations to experts indicated agreement of around 80.0% with both experts (p < 0.001). Notably, AI suggested more echocardiographies for minor CHD (64.7%) compared to experts (47.1%), and for major CHD, experts recommended performing FE in all cases (100%) while AI recommended in majority of cases (90.9%). Discrepancies between AI and experts are detailed and reviewed. CONCLUSIONS The evaluation found moderate agreement between AI and experts. Contextual misunderstandings and lack of specialized medical knowledge limit AI, necessitating clinical guideline guidance. Despite shortcomings, AI's referrals comprised 65% of minor CHD cases versus experts 47%, suggesting its potential as a cautious decision aid for clinicians.
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Affiliation(s)
- Lital Gordin Kopylov
- Obstetrical Unit, Shamir Medical Center (formerly Assaf Harofeh Medical Center), Zerifin, Israel
- Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Itai Goldrat
- Obstetrical Unit, Shamir Medical Center (formerly Assaf Harofeh Medical Center), Zerifin, Israel
- Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ron Maymon
- Obstetrical Unit, Shamir Medical Center (formerly Assaf Harofeh Medical Center), Zerifin, Israel
- Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ran Svirsky
- Department of Obstetrics and Gynecology, Genetic Unit, Samson Assuta Ashdod University Hospital, Affiliated to the Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- The Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Yifat Wiener
- Obstetrical Unit, Shamir Medical Center (formerly Assaf Harofeh Medical Center), Zerifin, Israel
- Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Sami Sagol AI Hub, ARC, Chaim Sheba Medical Center, Tel Hashomer, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Díaz O, Rodríguez-Ruíz A, Sechopoulos I. Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects. Eur J Radiol 2024; 175:111457. [PMID: 38640824 DOI: 10.1016/j.ejrad.2024.111457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 04/08/2024] [Indexed: 04/21/2024]
Abstract
PURPOSE This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. METHODS The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches. RESULTS DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption. CONCLUSIONS AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.
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Affiliation(s)
- Oliver Díaz
- Artificial Intelligence in Medicine Laboratory, Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain; Computer Vision Center, Barcelona, Spain.
| | | | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands; Dutch Expert Centre for Screening (LRCB), Nijmegen, the Netherlands; Technical Medicine Center, University of Twente, Enschede, the Netherlands.
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Lee SE, Hong H, Kim EK. Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography. Eur J Radiol Open 2024; 12:100545. [PMID: 38293282 PMCID: PMC10825593 DOI: 10.1016/j.ejro.2023.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/27/2023] [Accepted: 12/29/2023] [Indexed: 02/01/2024] Open
Abstract
Purpose To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.
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Affiliation(s)
| | | | - Eun-Kyung Kim
- Correspondence to: Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gul̥, Yongin-si, Gyeonggi-do, Korea.
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Perez-Lopez R, Ghaffari Laleh N, Mahmood F, Kather JN. A guide to artificial intelligence for cancer researchers. Nat Rev Cancer 2024; 24:427-441. [PMID: 38755439 DOI: 10.1038/s41568-024-00694-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/09/2024] [Indexed: 05/18/2024]
Abstract
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.
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Affiliation(s)
- Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Narmin Ghaffari Laleh
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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Weigel S, Katalinic A. [Structured screening for sporadic breast cancer]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:463-470. [PMID: 38499691 DOI: 10.1007/s00117-024-01283-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND The aim of secondary prevention of breast cancer is to detect the disease at the earliest curable stage and thus to reduce breast cancer-specific mortality. To this end, the nationwide population-based mammography screening program (MSP) was set up in Germany in 2005 in addition to an interdisciplinary prevention project for high-risk groups. OBJECTIVE Overview of the current state of the MSP, the upcoming age expansion, and potential further developments. MATERIAL AND METHODS Narrative review article with topic-guided literature and data search. RESULTS Approximately 50% of the 70,500 new cases of breast cancer that occur each year are related to the age group of the MSP. 10 years after introduction of the MSP, the incidence of advanced breast cancer stages and breast cancer-related mortality of the screening target group have steadily decreased by about one quarter, while no relevant trends were seen in the neighboring age groups at the population level. CONCLUSION The MSP has effectively contributed to a reduction of breast cancer mortality. With the expansion of the age groups to 45-75 years, more women have access to structured, quality assured screening. With the use of advanced stratifications and diagnostics as well as artificial intelligence, the MSP could be further optimized.
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Affiliation(s)
- Stefanie Weigel
- Klinik für Radiologie und Referenzzentrum Mammographie Münster, Universität Münster und Universitätsklinikum Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Deutschland.
| | - Alexander Katalinic
- Institut für Sozialmedizin und Epidemiologie, Universitätsklinikum Schleswig-Holstein, Campus Lübeck und Universität zu Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Deutschland
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48
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Mann RM, Teuwen J. Beyond the AJR: A Breakthrough in the Use of Artificial Intelligence for Mammography in Screening for Breast Cancer. AJR Am J Roentgenol 2024; 222:e2330359. [PMID: 37850578 DOI: 10.2214/ajr.23.30359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Affiliation(s)
- Ritse M Mann
- Department of Medical Imaging, Radboud University Medical Center, 10 Geert Grooteplein, Nijmegen, 6525 GS, The Netherlands
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Center, 10 Geert Grooteplein, Nijmegen, 6525 GS, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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Kühl J, Elhakim MT, Stougaard SW, Rasmussen BSB, Nielsen M, Gerke O, Larsen LB, Graumann O. Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms. Eur Radiol 2024; 34:3935-3946. [PMID: 37938386 PMCID: PMC11166831 DOI: 10.1007/s00330-023-10423-7] [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: 07/04/2023] [Revised: 10/09/2023] [Accepted: 10/14/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists. MATERIALS AND METHODS All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AIsens) and specificity (AIspec) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR). RESULTS The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AIsens had lower specificity (97.5% vs 97.7%; p < 0.0001) and PPV (17.5% vs 18.7%; p = 0.01) and a higher RR (3.0% vs 2.8%; p < 0.0001) than first readers. AIspec was comparable to first readers in terms of all accuracy measures. Both AIsens and AIspec detected significantly fewer screen-detected cancers (1166 (AIsens), 1156 (AIspec) vs 1252; p < 0.0001) but found more interval cancers compared to first readers (126 (AIsens), 117 (AIspec) vs 39; p < 0.0001) with varying types of cancers detected across multiple subgroups. CONCLUSION Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers. CLINICAL RELEVANCE STATEMENT Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly. KEY POINTS • Standalone AI cancer detection was compared to first readers in a double-read mammography screening population. • Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers. • With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics.
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Affiliation(s)
- Johanne Kühl
- Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark
| | - Mohammad Talal Elhakim
- Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.
- Department of Radiology, Odense University Hospital, Kløvervænget 47, Ground Floor, 5000, Odense C, Denmark.
| | - Sarah Wordenskjold Stougaard
- Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark
| | - Benjamin Schnack Brandt Rasmussen
- Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark
- Department of Radiology, Odense University Hospital, Kløvervænget 47, Ground Floor, 5000, Odense C, Denmark
- CAI-X - Centre for Clinical Artificial Intelligence, Odense University Hospital, Kløvervænget 8C, 5000, Odense C, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark
| | - Oke Gerke
- Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Kløvervænget 47, 5000, Odense C, Denmark
| | - Lisbet Brønsro Larsen
- Department of Radiology, Odense University Hospital, Kløvervænget 47, Ground Floor, 5000, Odense C, Denmark
| | - Ole Graumann
- Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark
- Department of Radiology, Aarhus University Hospital, Palle Juul-Jensens Blvd. 99, 8200, Aarhus N, Denmark
- Department of Clinical Research, Aarhus University, Palle Juul-Jensens Blvd. 99, 8200, Aarhus N, Denmark
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Holen ÅS, Martiniussen MA, Bergan MB, Moshina N, Hovda T, Hofvind S. Women's attitudes and perspectives on the use of artificial intelligence in the assessment of screening mammograms. Eur J Radiol 2024; 175:111431. [PMID: 38520804 DOI: 10.1016/j.ejrad.2024.111431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/26/2024] [Accepted: 03/15/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE To investigate attitudes and perspectives on the use of artificial intelligence (AI) in the assessment of screening mammograms among women invited to BreastScreen Norway. METHOD An anonymous survey was sent to all women invited to BreastScreen Norway during the study period, October 10, 2022, to December 25, 2022 (n = 84,543). Questions were answered on a 10-point Likert scale and as multiple-choice, addressing knowledge of AI, willingness to participate in AI studies, information needs, confidence in AI results and AI assisted reading strategies, and thoughts on concerns and benefits of AI in mammography screening. Analyses were performed using χ2 and logistic regression tests. RESULTS General knowledge of AI was reported as extensive by 11.0% of the 8,355 respondents. Respondents were willing to participate in studies using AI either for decision support (64.0%) or triaging (54.9%). Being informed about use of AI-assisted image assessment was considered important, and a reading strategy of AI in combination with one radiologist preferred. Having extensive knowledge of AI was associated with willingness to participate in AI studies (decision support; odds ratio [OR]: 5.1, 95% confidence interval [CI]: 4.1-6.4, and triaging; OR: 3.4, 95% CI: 2.8-4.0) and trust in AI's independent assessment (OR: 6.8, 95% CI: 5.7, 8.3). CONCLUSIONS Women invited to BreastScreen Norway had a positive attitude towards the use of AI in image assessment, given that human readers are still involved. Targeted information and increased public knowledge of AI could help achieve high participation in AI studies and successful implementation of AI in mammography screening.
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Affiliation(s)
- Åsne Sørlien Holen
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
| | - Marit Almenning Martiniussen
- Department of Radiology, Østfold Hospital Trust, Kalnes, Norway; University of Oslo, Institute of Clinical Medicine, Oslo, Norway.
| | - Marie Burns Bergan
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
| | - Nataliia Moshina
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
| | - Tone Hovda
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway.
| | - Solveig Hofvind
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway; Department of Health and Care Sciences, UiT, The Artic University of Norway, Tromsø, Norway.
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