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Clunie D, Prior F, Rutherford M, Moore S, Parker W, Kondylakis H, Ludwigs C, Klenk J, Lou B, O'Sullivan LT, Marcus D, Dobes J, Gutman A, Farahani K. Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01182-y. [PMID: 38997571 DOI: 10.1007/s10278-024-01182-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024]
Abstract
De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review. The topics covered included the report of the Medical Image De-Identification Initiative (MIDI) Task Group on best practices and recommendations, tools for conventional approaches to de-identification, international approaches to de-identification, and an industry panel.
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Affiliation(s)
| | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | - Stephen Moore
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | - Haridimos Kondylakis
- Institute of Computer Science, Foundation of Research & Technology - Hellas (FORTH), Heraklion, Greece
| | | | | | - Bob Lou
- Google, Mountain View, CA, USA
| | | | | | | | | | - Keyvan Farahani
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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2
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Clunie D, Taylor A, Bisson T, Gutman D, Xiao Y, Schwarz CG, Greve D, Gichoya J, Shih G, Kline A, Kopchick B, Farahani K. Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01183-x. [PMID: 38980626 DOI: 10.1007/s10278-024-01183-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024]
Abstract
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the United States National Cancer Institute (NCI) convened a two half-day virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the second day of the workshop, the recordings and presentations of which are publicly available for review. The topics covered included pathology whole slide image de-identification, de-facing, the role of AI in image de-identification, and the NCI Medical Image De-Identification Initiative (MIDI) datasets and pipeline.
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Affiliation(s)
| | | | - Tom Bisson
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Ying Xiao
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - George Shih
- Weill Cornell Medical College, New York, NY, USA
| | | | | | - Keyvan Farahani
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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3
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Ran Y, Qin W, Qin C, Li X, Liu Y, Xu L, Mu X, Yan L, Wang B, Dai Y, Chen J, Han D. A high-quality dataset featuring classified and annotated cervical spine X-ray atlas. Sci Data 2024; 11:625. [PMID: 38871800 PMCID: PMC11176335 DOI: 10.1038/s41597-024-03383-0] [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: 01/23/2024] [Accepted: 05/15/2024] [Indexed: 06/15/2024] Open
Abstract
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm's development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.
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Affiliation(s)
- Yu Ran
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Wanli Qin
- Department of Dermatology, Air Force Medical Center, Air Force Medical University, Beijing, 710000, China
| | - Changlong Qin
- Department of Orthopedics and Traumatology, Qiannan Traditional Chinese Medicine Hospital, Guizhou, 558000, China
| | - Xiaobin Li
- Shenzhen Hospital of Beijing University of Chinese Medicine, Shenzhen, 518172, China
| | - Yixing Liu
- School of Management, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Lin Xu
- Department of Orthopedics, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Xiaohong Mu
- Department of Orthopedics, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Li Yan
- School of Humanities, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Bei Wang
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Yuxiang Dai
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Jiang Chen
- Department of Orthopedics, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
| | - Dongran Han
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 102488, China.
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4
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Bartusik-Aebisher D, Mytych W, Dynarowicz K, Myśliwiec A, Machorowska-Pieniążek A, Cieślar G, Kawczyk-Krupka A, Aebisher D. Magnetic Resonance Imaging in Breast Cancer Tissue In Vitro after PDT Therapy. Diagnostics (Basel) 2024; 14:563. [PMID: 38473036 DOI: 10.3390/diagnostics14050563] [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: 02/06/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024] Open
Abstract
Photodynamic therapy (PDT) is increasingly used in modern medicine. It has found application in the treatment of breast cancer. The most common cancer among women is breast cancer. We collected cancer cells from the breast from the material received after surgery. We focused on tumors that were larger than 10 mm in size. Breast cancer tissues for this quantitative non-contrast magnetic resonance imaging (MRI) study could be seen macroscopically. The current study aimed to present findings on quantitative non-contrast MRI of breast cancer cells post-PDT through the evaluation of relaxation times. The aim of this work was to use and optimize a 1.5 T MRI system. MRI tests were performed using a clinical scanner, namely the OPTIMA MR360 manufactured by General Electric HealthCare. The work included analysis of T1 and T2 relaxation times. This analysis was performed using the MATLAB package (produced by MathWorks). The created application is based on medical MRI images saved in the DICOM3.0 standard. T1 and T2 measurements were subjected to the Shapiro-Wilk test, which showed that both samples belonged to a normal distribution, so a parametric t-test for dependent samples was used to test for between-sample variability. The study included 30 sections tested in 2 stages, with consistent technical parameters. For T1 measurements, 12 scans were performed with varying repetition times (TR) and a constant echo time (TE) of 3 ms. For T2 measurements, 12 scans were performed with a fixed repetition time of 10,000 ms and varying echo times. After treating samples with PpIX disodium salt and bubbling with pure oxygen, PDT irradiation was applied. The cell relaxation time after therapy was significantly shorter than the cell relaxation time before PDT. The cells were exposed to PpIX disodium salt as the administered pharmacological substance. The study showed that the therapy significantly affected tumor cells, which was confirmed by a significant reduction in tumor cell relaxation time on the MRI results.
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Affiliation(s)
- Dorota Bartusik-Aebisher
- Department of Biochemistry and General Chemistry, Medical College of the University of Rzeszów, 35-959 Rzeszów, Poland
| | - Wiktoria Mytych
- Students English Division Science Club, Medical College of the University of Rzeszów, 35-959 Rzeszów, Poland
| | - Klaudia Dynarowicz
- Center for Innovative Research in Medical and Natural Sciences, Medical College of the University of Rzeszów, 35-310 Rzeszów, Poland
| | - Angelika Myśliwiec
- Center for Innovative Research in Medical and Natural Sciences, Medical College of the University of Rzeszów, 35-310 Rzeszów, Poland
| | | | - Grzegorz Cieślar
- Department of Internal Medicine, Angiology and Physical Medicine, Center for Laser Diagnostics and Therapy, Medical University of Silesia in Katowice, Batorego 15 Street, 41-902 Bytom, Poland
| | - Aleksandra Kawczyk-Krupka
- Department of Internal Medicine, Angiology and Physical Medicine, Center for Laser Diagnostics and Therapy, Medical University of Silesia in Katowice, Batorego 15 Street, 41-902 Bytom, Poland
| | - David Aebisher
- Department of Photomedicine and Physical Chemistry, Medical College of the University of Rzeszów, 35-310 Rzeszów, Poland
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Khosravi B, Mickley JP, Rouzrokh P, Taunton MJ, Larson AN, Erickson BJ, Wyles CC. Anonymizing Radiographs Using an Object Detection Deep Learning Algorithm. Radiol Artif Intell 2023; 5:e230085. [PMID: 38074777 PMCID: PMC10698585 DOI: 10.1148/ryai.230085] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 08/11/2023] [Accepted: 08/25/2023] [Indexed: 02/02/2024]
Abstract
Radiographic markers contain protected health information that must be removed before public release. This work presents a deep learning algorithm that localizes radiographic markers and selectively removes them to enable de-identified data sharing. The authors annotated 2000 hip and pelvic radiographs to train an object detection computer vision model. Data were split into training, validation, and test sets at the patient level. Extracted markers were then characterized using an image processing algorithm, and potentially useful markers (eg, "L" and "R") without identifying information were retained. The model achieved an area under the precision-recall curve of 0.96 on the internal test set. The de-identification accuracy was 100% (400 of 400), with a de-identification false-positive rate of 1% (eight of 632) and a retention accuracy of 93% (359 of 386) for laterality markers. The algorithm was further validated on an external dataset of chest radiographs, achieving a de-identification accuracy of 96% (221 of 231). After fine-tuning the model on 20 images from the external dataset to investigate the potential for improvement, a 99.6% (230 of 231, P = .04) de-identification accuracy and decreased false-positive rate of 5% (26 of 512) were achieved. These results demonstrate the effectiveness of a two-pass approach in image de-identification. Keywords: Conventional Radiography, Skeletal-Axial, Thorax, Experimental Investigations, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Chang and Li in this issue.
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Affiliation(s)
| | | | - Pouria Rouzrokh
- From the Orthopedic Surgery Artificial Intelligence Laboratory,
Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.),
Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.),
Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of
Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN
55905
| | - Michael J. Taunton
- From the Orthopedic Surgery Artificial Intelligence Laboratory,
Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.),
Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.),
Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of
Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN
55905
| | - A. Noelle Larson
- From the Orthopedic Surgery Artificial Intelligence Laboratory,
Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.),
Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.),
Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of
Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN
55905
| | - Bradley J. Erickson
- From the Orthopedic Surgery Artificial Intelligence Laboratory,
Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.),
Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.),
Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of
Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN
55905
| | - Cody C. Wyles
- From the Orthopedic Surgery Artificial Intelligence Laboratory,
Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.),
Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.),
Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of
Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN
55905
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6
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Osuala R, Skorupko G, Lazrak N, Garrucho L, García E, Joshi S, Jouide S, Rutherford M, Prior F, Kushibar K, Díaz O, Lekadir K. medigan: a Python library of pretrained generative models for medical image synthesis. J Med Imaging (Bellingham) 2023; 10:061403. [PMID: 36814939 PMCID: PMC9940031 DOI: 10.1117/1.jmi.10.6.061403] [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: 10/05/2022] [Accepted: 01/23/2023] [Indexed: 02/22/2023] Open
Abstract
Purpose Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data. Approach We propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. Results The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors. Conclusion medigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.
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Affiliation(s)
- Richard Osuala
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Grzegorz Skorupko
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Noussair Lazrak
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Lidia Garrucho
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Eloy García
- Universitat de Barcelona, Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Smriti Joshi
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Socayna Jouide
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Michael Rutherford
- University of Arkansas for Medical Sciences, Department of Biomedical Informatics, Little Rock, Arkansas, United States
| | - Fred Prior
- University of Arkansas for Medical Sciences, Department of Biomedical Informatics, Little Rock, Arkansas, United States
| | - Kaisar Kushibar
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Oliver Díaz
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Karim Lekadir
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
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Li LT, Haley LC, Boyd AK, Bernstam EV. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. J Biomed Inform 2023; 147:104531. [PMID: 37884177 DOI: 10.1016/j.jbi.2023.104531] [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: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges. METHODS We conducted a literature search in PubMed (1/1/2001-1/1/2023). The search was restricted to publications in the English language, and human study subjects. We excluded articles that did not discuss AI, machine learning, predictive analytics, and barriers to the use of these techniques in health care. Using grounded theory methodology, we abstracted concepts to identify major barriers to AI use in medicine. RESULTS We identified a total of 2,382 articles. After reviewing the 306 included papers, we developed 19 major themes, which we categorized into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). These themes included: Lack of Explainability, Need for Validation Protocols, Need for Standards for Interoperability, Need for Reporting Guidelines, Need for Standardization of Performance Metrics, Lack of Plan for Updating Algorithm, Job Loss, Skills Loss, Workflow Challenges, Loss of Patient Autonomy and Consent, Disturbing the Patient-Clinician Relationship, Lack of Trust in AI, Logistical Challenges, Lack of strategic plan, Lack of Cost-effectiveness Analysis and Proof of Efficacy, Privacy, Liability, Bias and Social Justice, and Education. CONCLUSION We identified 19 major barriers to the use of AI in healthcare and categorized them into three levels: the Technical/Algorithm, Stakeholder, and Social levels (TASS). Future studies should expand on barriers in pediatric care and focus on developing clearly defined protocols to overcome these barriers.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Division of Pediatric Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, United States; McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
| | - Lauren C Haley
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Alexandra K Boyd
- McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
| | - Elmer V Bernstam
- McWilliams School of Biomedical Informatics at UT Health Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States; McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States.
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8
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Xiao D, Zhu F, Jiang J, Niu X. Leveraging natural cognitive systems in conjunction with ResNet50-BiGRU model and attention mechanism for enhanced medical image analysis and sports injury prediction. Front Neurosci 2023; 17:1273931. [PMID: 37795185 PMCID: PMC10546033 DOI: 10.3389/fnins.2023.1273931] [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: 08/07/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023] Open
Abstract
Introduction In this study, we explore the potential benefits of integrating natural cognitive systems (medical professionals' expertise) and artificial cognitive systems (deep learning models) in the realms of medical image analysis and sports injury prediction. We focus on analyzing medical images of athletes to gain valuable insights into their health status. Methods To synergize the strengths of both natural and artificial cognitive systems, we employ the ResNet50-BiGRU model and introduce an attention mechanism. Our goal is to enhance the performance of medical image feature extraction and motion injury prediction. This integrated approach aims to achieve precise identification of anomalies in medical images, particularly related to muscle or bone damage. Results We evaluate the effectiveness of our method on four medical image datasets, specifically pertaining to skeletal and muscle injuries. We use performance indicators such as Peak Signal-to-Noise Ratio and Structural Similarity Index, confirming the robustness of our approach in sports injury analysis. Discussion Our research contributes significantly by providing an effective deep learning-driven method that harnesses both natural and artificial cognitive systems. By combining human expertise with advanced machine learning techniques, we offer a comprehensive understanding of athletes' health status. This approach holds potential implications for enhancing sports injury prevention, improving diagnostic accuracy, and tailoring personalized treatment plans for athletes, ultimately promoting better overall health and performance outcomes. Despite advancements in medical image analysis and sports injury prediction, existing systems often struggle to identify subtle anomalies and provide precise injury risk assessments, underscoring the necessity of a more integrated and comprehensive approach.
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Affiliation(s)
- Duo Xiao
- Ministry of Culture, Sports and Labor, Jiangxi Gannan Health Vocational College, Ganzhou, Jiangxi, China
| | - Fei Zhu
- Ministry of Culture, Sports and Labor, Jiangxi Gannan Health Vocational College, Ganzhou, Jiangxi, China
| | - Jian Jiang
- Gannan University of Science and Technology, Ganzhou, Jiangxi, China
| | - Xiaoqiang Niu
- Ministry of Culture, Sports and Labor, Jiangxi Gannan Health Vocational College, Ganzhou, Jiangxi, China
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9
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Clunie DA, Flanders A, Taylor A, Erickson B, Bialecki B, Brundage D, Gutman D, Prior F, Seibert JA, Perry J, Gichoya JW, Kirby J, Andriole K, Geneslaw L, Moore S, Fitzgerald TJ, Tellis W, Xiao Y, Farahani K, Luo J, Rosenthal A, Kandarpa K, Rosen R, Goetz K, Babcock D, Xu B, Hsiao J. Report of the Medical Image De-Identification (MIDI) Task Group - Best Practices and Recommendations. ARXIV 2023:arXiv:2303.10473v2. [PMID: 37033463 PMCID: PMC10081345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Affiliation(s)
| | | | | | | | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences
| | | | | | | | - Justin Kirby
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | - Ying Xiao
- University of Pennsylvania Health System
| | | | - James Luo
- National Heart, Lung, and Blood Institute (NHLBI)
| | - Alex Rosenthal
- National Institute of Allergy and Infectious Diseases (NIAID)
| | - Kris Kandarpa
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
| | - Rebecca Rosen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
| | | | - Debra Babcock
- National Institute of Neurological Disorders and Stroke (NINDS)
| | - Ben Xu
- National Institute on Alcohol Abuse and Alcoholism (NIAAA)
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10
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Gorman C, Punzo D, Octaviano I, Pieper S, Longabaugh WJR, Clunie DA, Kikinis R, Fedorov AY, Herrmann MD. Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nat Commun 2023; 14:1572. [PMID: 36949078 PMCID: PMC10033920 DOI: 10.1038/s41467-023-37224-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
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Affiliation(s)
- Chris Gorman
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Y Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Shahid A, Bazargani MH, Banahan P, Mac Namee B, Kechadi T, Treacy C, Regan G, MacMahon P. A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis. Healthcare (Basel) 2022; 10:755. [PMID: 35627892 PMCID: PMC9141493 DOI: 10.3390/healthcare10050755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/17/2022] Open
Abstract
Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients' Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute. In this paper, we first highlight and review the key challenges in the medical image data de-identification process. In this paper, we develop a two-stage de-identification process for CT scan images available in DICOM file format. In the first stage of the de-identification process, the patient's PII-including name, date of birth, etc., are removed at the hospital facility using the export process available in their Picture Archiving and Communication System (PACS). The second stage employs the proposed DICOM de-identification tool for an exhaustive attribute-level investigation to further de-identify and ensure that all PII has been removed. Finally, we provide a roadmap for future considerations to build a semi-automated or automated tool for the DICOM datasets de-identification.
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Affiliation(s)
- Arsalan Shahid
- School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland; (M.H.B.); (B.M.N.); (T.K.)
| | - Mehran H. Bazargani
- School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland; (M.H.B.); (B.M.N.); (T.K.)
| | - Paul Banahan
- Department of Radiology, Mater Misericordiae University Hospital, D07 R2WY Dublin, Ireland; (P.B.); (P.M.)
| | - Brian Mac Namee
- School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland; (M.H.B.); (B.M.N.); (T.K.)
| | - Tahar Kechadi
- School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland; (M.H.B.); (B.M.N.); (T.K.)
| | - Ceara Treacy
- Regulated Software Research Centre, Dundalk Institute of Technology, A91 K584 Dundalk, Ireland; (C.T.); (G.R.)
| | - Gilbert Regan
- Regulated Software Research Centre, Dundalk Institute of Technology, A91 K584 Dundalk, Ireland; (C.T.); (G.R.)
| | - Peter MacMahon
- Department of Radiology, Mater Misericordiae University Hospital, D07 R2WY Dublin, Ireland; (P.B.); (P.M.)
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Dagenais S, Russo L, Madsen A, Webster J, Becnel L. Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin Pharmacol Ther 2022; 111:77-89. [PMID: 34839524 PMCID: PMC9299990 DOI: 10.1002/cpt.2480] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 10/30/2021] [Indexed: 12/28/2022]
Abstract
Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.
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Affiliation(s)
| | - Leo Russo
- Global Medical Epidemiology, Worldwide Medical and SafetyPfizer IncCollegevillePennsylvaniaUSA
| | - Ann Madsen
- Global Medical Epidemiology, Worldwide Medical and SafetyPfizer IncNew YorkNew YorkUSA
| | - Jen Webster
- Real World EvidencePfizer IncNew YorkNew YorkUSA
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Jin Y, Xu Y, Li Y, Chen R, Cai W. Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients. Front Oncol 2021; 11:755271. [PMID: 34804945 PMCID: PMC8602567 DOI: 10.3389/fonc.2021.755271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 09/27/2021] [Indexed: 12/24/2022] Open
Abstract
Gastric cancer (GC) is a typical heterogeneous malignant tumor, whose insensitivity to chemotherapy is a common cause of tumor recurrence and metastasis. There is no doubt regarding the effectiveness of adjuvant chemotherapy (ACT) for GC, but the population for whom it is indicated and the selection of specific options remain the focus of present research. The conventional pathological TNM prediction focuses on cancer cells to predict prognosis, while they do not provide sufficient prediction. Enhanced computed tomography (CT) scanning is a validated tool that assesses the involvement of careful identification of the tumor, lymph node involvement, and metastatic spread. Using the radiomics approach, we selected the least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for predicting the overall survival (OS) and disease-free survival (DFS) of patients with complete postoperative gastric cancer and further identifying candidate benefits from ACT. The radiomics trait-associated genes captured clinically relevant molecular pathways and potential chemotherapeutic drug metabolism mechanisms. Our results of precise surrogates using radiogenomics can lead to additional benefit from adjuvant chemotherapy and then survival prediction in postoperative GC patients.
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Affiliation(s)
- Yin Jin
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yilun Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yanyan Li
- Department of Urology, Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Renpin Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weiyang Cai
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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