1
|
Knight E, Rhinehart T, de Zwaan DR, Weldy MJ, Cartwright M, Hawley SH, Larkin JL, Lesmeister D, Bayne E, Kitzes J. Individual identification in acoustic recordings. Trends Ecol Evol 2024:S0169-5347(24)00118-6. [PMID: 38862357 DOI: 10.1016/j.tree.2024.05.007] [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: 10/30/2023] [Revised: 05/08/2024] [Accepted: 05/15/2024] [Indexed: 06/13/2024]
Abstract
Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.
Collapse
Affiliation(s)
- Elly Knight
- Department of Biological Sciences, Alberta Biodiversity Monitoring Institute, University of Alberta, Edmonton, Alberta, T6G 2E6, Canada.
| | - Tessa Rhinehart
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
| | - Devin R de Zwaan
- Department of Biology, Mount Allison University, Sackville, NB, E4L 1E4, Canada; Acadia University, Wolfville, NS, B4P 2R6, Canada
| | - Matthew J Weldy
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97331-5704, USA
| | - Mark Cartwright
- Department of Informatics, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Scott H Hawley
- Chemistry and Physics Department, Belmont University, Nashville, TN, 37212, USA
| | - Jeffery L Larkin
- Department of Biology, Indiana University of Pennsylvania, Indiana, PA, 15705-1081, USA; American Bird Conservancy, The Plains, VA, 20198, USA
| | - Damon Lesmeister
- USDA Forest Service, Pacific Northwest Research Station, Corvallis Forestry Science Laboratory, Oregon State University, Corvallis, OR, 97330, USA
| | - Erin Bayne
- Department of Biological Sciences, Alberta Biodiversity Monitoring Institute, University of Alberta, Edmonton, Alberta, T6G 2E6, Canada
| | - Justin Kitzes
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| |
Collapse
|
2
|
Schmidt C, Boissonnet T, Dohle J, Bernhardt K, Ferrando-May E, Wernet T, Nitschke R, Kunis S, Weidtkamp-Peters S. A practical guide to bioimaging research data management in core facilities. J Microsc 2024; 294:350-371. [PMID: 38752662 DOI: 10.1111/jmi.13317] [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: 04/09/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/21/2024]
Abstract
Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.
Collapse
Affiliation(s)
- Christian Schmidt
- Enabling Technology Department, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tom Boissonnet
- Center for Advanced Imaging, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julia Dohle
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | - Karen Bernhardt
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | - Elisa Ferrando-May
- Enabling Technology Department, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Tobias Wernet
- Life Imaging Center, University of Freiburg, Freiburg, Germany
| | - Roland Nitschke
- Life Imaging Center, University of Freiburg, Freiburg, Germany
- CIBSS and BIOSS - Centres for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Susanne Kunis
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | | |
Collapse
|
3
|
Woodworth CF, Frota Lima LM, Bartholmai BJ, Koo CW. Imaging of Solid Pulmonary Nodules. Clin Chest Med 2024; 45:249-261. [PMID: 38816086 DOI: 10.1016/j.ccm.2023.08.013] [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: 06/01/2024]
Abstract
Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.
Collapse
Affiliation(s)
- Claire F Woodworth
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Livia Maria Frota Lima
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Brian J Bartholmai
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
| |
Collapse
|
4
|
Dulaney A, Virostko J. Disparities in the Demographic Composition of The Cancer Imaging Archive. Radiol Imaging Cancer 2024; 6:e230100. [PMID: 38240671 PMCID: PMC10825717 DOI: 10.1148/rycan.230100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/31/2023] [Accepted: 11/30/2023] [Indexed: 01/23/2024]
Abstract
Purpose To characterize the demographic distribution of The Cancer Imaging Archive (TCIA) studies and compare them with those of the U.S. cancer population. Materials and Methods In this retrospective study, data from TCIA studies were examined for the inclusion of demographic information. Of 189 studies in TCIA up until April 2023, a total of 83 human cancer studies were found to contain supporting demographic data. The median patient age and the sex, race, and ethnicity proportions of each study were calculated and compared with those of the U.S. cancer population, provided by the Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention U.S. Cancer Statistics Data Visualizations Tool. Results The median age of TCIA patients was found to be 6.84 years lower than that of the U.S. cancer population (P = .047) and contained more female than male patients (53% vs 47%). American Indian and Alaska Native, Black or African American, and Hispanic patients were underrepresented in TCIA studies by 47.7%, 35.8%, and 14.7%, respectively, compared with the U.S. cancer population. Conclusion The results demonstrate that the patient demographics of TCIA data sets do not reflect those of the U.S. cancer population, which may decrease the generalizability of artificial intelligence radiology tools developed using these imaging data sets. Keywords: Ethics, Meta-Analysis, Health Disparities, Cancer Health Disparities, Machine Learning, Artificial Intelligence, Race, Ethnicity, Sex, Age, Bias Published under a CC BY 4.0 license.
Collapse
Affiliation(s)
- Aidan Dulaney
- From the Department of Diagnostic Medicine (A.D., J.V.), Livestrong
Cancer Institutes (J.V.), and Department of Oncology (J.V.), Dell Medical
School, University of Texas at Austin, 210 E 24th St, Austin, TX 78712; and Oden
Institute for Computational Engineering and Sciences, University of Texas at
Austin, Austin, Tex (J.V.)
| | - John Virostko
- From the Department of Diagnostic Medicine (A.D., J.V.), Livestrong
Cancer Institutes (J.V.), and Department of Oncology (J.V.), Dell Medical
School, University of Texas at Austin, 210 E 24th St, Austin, TX 78712; and Oden
Institute for Computational Engineering and Sciences, University of Texas at
Austin, Austin, Tex (J.V.)
| |
Collapse
|
5
|
O'Shea R, Manickavasagar T, Horst C, Hughes D, Cusack J, Tsoka S, Cook G, Goh V. Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images. Insights Imaging 2023; 14:195. [PMID: 37980637 PMCID: PMC10657919 DOI: 10.1186/s13244-023-01542-2] [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: 05/23/2023] [Accepted: 10/13/2023] [Indexed: 11/21/2023] Open
Abstract
PURPOSE Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels ("image contains object" or "image does not contain object"), presenting a different approach towards explainable object detectors for radiological imaging tasks. METHODS A weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data. WSUnet generates voxel probability maps with a Unet and then constructs an image-level prediction by global max-pooling, thereby facilitating image-level training. WSUnet's voxel-level predictions were compared to traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions. RESULTS Despite the absence of voxel-level labels in training, WSUnet's voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76-0.80]; dice: 0.43, 95% CI: [0.39-0.46]), and external testing (precision: 0.78, 95% CI: [0.76-0.81]; dice: 0.33, 95% CI: [0.32-0.35]). WSUnet's voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49-0.56] vs. 0.23, 95% CI: [0.21-0.25]) and testing (AUPR: 0.40, 95% CI: [0.38-0.41] vs. 0.36, 95% CI: [0.34-0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68-0.77]). CONCLUSION Weakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging. CRITICAL RELEVANCE STATEMENT WSUnet learns to segment images at voxel level, training only with image-level labels. A Unet backbone first generates a voxel-level probability map and then extracts the maximum voxel prediction as the image-level prediction. Thus, training uses only image-level annotations, reducing human workload. WSUnet's voxel-level predictions provide a causally verifiable explanation for its image-level prediction, improving interpretability. KEY POINTS • Explainability and interpretability are essential for reliable medical image classifiers. • This study applies weakly supervised segmentation to generate explainable image classifiers. • The weakly supervised Unet inherently explains its image-level predictions at voxel level.
Collapse
Affiliation(s)
- Robert O'Shea
- Department of Cancer Imaging, King's College London, London, UK.
| | | | - Carolyn Horst
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Daniel Hughes
- Department of Cancer Imaging, King's College London, London, UK
| | - James Cusack
- Department of Radiology, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Sophia Tsoka
- Department of Natural and Mathematical Sciences, King's College London, London, UK
| | - Gary Cook
- King's College London & Guy's and St Thomas' PET Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Vicky Goh
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
FitzGerald TJ, Bishop-Jodoin M, Laurie F, Iandoli M, Smith K, Ulin K, Ding L, Moni J, Cicchetti MG, Knopp M, Kry S, Xiao Y, Rosen M, Prior F, Saltz J, Michalski J. The Importance of Quality Assurance in Radiation Oncology Clinical Trials. Semin Radiat Oncol 2023; 33:395-406. [PMID: 37684069 DOI: 10.1016/j.semradonc.2023.06.005] [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: 09/10/2023]
Abstract
Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.
Collapse
Affiliation(s)
- Thomas J FitzGerald
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA..
| | | | - Fran Laurie
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Matthew Iandoli
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Koren Smith
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Kenneth Ulin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Linda Ding
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Janaki Moni
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - M Giulia Cicchetti
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Michael Knopp
- Department of Radiology, University of Cincinnati, Cincinnati, OH
| | - Stephen Kry
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA
| | - Mark Rosen
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Jeff Michalski
- Department of Radiation Oncology, Washington University in St Louis, St Louis, MO
| |
Collapse
|
8
|
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)
| | | |
Collapse
|
9
|
Korneev A, Lipina M, Lychagin A, Timashev P, Kon E, Telyshev D, Goncharuk Y, Vyazankin I, Elizarov M, Murdalov E, Pogosyan D, Zhidkov S, Bindeeva A, Liang XJ, Lasovskiy V, Grinin V, Anosov A, Kalinsky E. Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon? INTERNATIONAL ORTHOPAEDICS 2023; 47:393-403. [PMID: 36369394 DOI: 10.1007/s00264-022-05628-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE This study aims to describe and assess the current stage of the artificial intelligence (AI) technology integration in preventive orthopaedics of the knee and hip joints. MATERIALS AND METHODS The study was conducted in strict compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Literature databases were searched for articles describing the development and validation of AI models aimed at diagnosing knee or hip joint pathologies or predicting their development or course in patients. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and QUADAS-AI tools. RESULTS 56 articles were found that meet all the inclusion criteria. We identified two problems that block the full integration of AI into the routine of an orthopaedic physician. The first of them is related to the insufficient amount, variety and quality of data for training, and validation and testing of AI models. The second problem is the rarity of rational evaluation of models, which is why their real quality cannot always be evaluated. CONCLUSION The vastness and relevance of the studied topic are beyond doubt. Qualitative and optimally validated models exist in all four scopes considered. Additional optimization and confirmation of the models' quality on various datasets are the last technical stumbling blocks for creating usable software and integrating them into the routine of an orthopaedic physician.
Collapse
Affiliation(s)
- Alexander Korneev
- Medical Polymer Synthesis Laboratory, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia.,Laboratory of Clinical Smart Nanotechnologies, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia.,N.V. Sklifosovsky Institute of Clinical Medicine, Sechenov University, Moscow, 119991, Russia
| | - Marina Lipina
- Laboratory of Clinical Smart Nanotechnologies, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia. .,Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia.
| | - Alexey Lychagin
- Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia
| | - Peter Timashev
- Laboratory of Clinical Smart Nanotechnologies, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov University, Moscow, 119991, Russia.,Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia
| | - Elizaveta Kon
- Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia.,Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Dmitry Telyshev
- Russia Institute of Biomedical Systems, National Research University of Electronic Technology Moscow, Zelenograd, 124498, Russia.,Institute of Bionic Technologies and Engineering, Sechenov University, Moscow, 119991, Russia
| | - Yuliya Goncharuk
- Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia
| | - Ivan Vyazankin
- Laboratory of Clinical Smart Nanotechnologies, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia.,Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia
| | - Mikhail Elizarov
- Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia
| | - Emirkhan Murdalov
- Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia
| | - David Pogosyan
- Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia.,Department of Life Safety and Disaster Medicine, Sechenov University, Moscow, 119991, Russia
| | - Sergei Zhidkov
- N.V. Sklifosovsky Institute of Clinical Medicine, Sechenov University, Moscow, 119991, Russia
| | - Anastasia Bindeeva
- N.V. Sklifosovsky Institute of Clinical Medicine, Sechenov University, Moscow, 119991, Russia
| | - Xing-Jie Liang
- Laboratory of Clinical Smart Nanotechnologies, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia.,CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vladimir Lasovskiy
- Department of Artificial Intelligence and Digital Products, VimpelCom, Moscow, 127083, Russia
| | - Victor Grinin
- Department of Artificial Intelligence and Digital Products, VimpelCom, Moscow, 127083, Russia
| | - Alexey Anosov
- Department of Artificial Intelligence and Digital Products, VimpelCom, Moscow, 127083, Russia
| | - Eugene Kalinsky
- Laboratory of Clinical Smart Nanotechnologies, Institute for Regenerative Medicine, Sechenov University, Moscow, 119991, Russia.,Department of Traumatology, Orthopaedics and Disaster Surgery, Sechenov University, Moscow, 119991, Russia
| |
Collapse
|
10
|
Matlock AD, Vaibhav V, Holewinski R, Venkatraman V, Dardov V, Manalo DM, Shelley B, Ornelas L, Banuelos M, Mandefro B, Escalante-Chong R, Li J, Finkbeiner S, Fraenkel E, Rothstein J, Thompson L, Sareen D, Svendsen CN, Van Eyk JE. NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency. Sci Data 2023; 10:24. [PMID: 36631473 PMCID: PMC9834231 DOI: 10.1038/s41597-022-01687-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/07/2022] [Indexed: 01/13/2023] Open
Abstract
The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.
Collapse
Affiliation(s)
- Andrea D Matlock
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Vineet Vaibhav
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Ronald Holewinski
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Vidya Venkatraman
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Victoria Dardov
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Danica-Mae Manalo
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Brandon Shelley
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Loren Ornelas
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Maria Banuelos
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Berhan Mandefro
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | | | - Jonathan Li
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA, 02142, USA
| | - Steve Finkbeiner
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ernest Fraenkel
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA, 02142, USA
| | - Jeffrey Rothstein
- NeuroLINCS, Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Leslie Thompson
- NeuroLINCS, Departments of Psychiatry and Human Behaviour, Neurobiology and Behaviour and UCI MIND, University of California Irvine, Irvine, CA, 92697, USA
| | - Dhruv Sareen
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Clive N Svendsen
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Jennifer E Van Eyk
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
| |
Collapse
|
11
|
Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:795-816. [PMID: 36654817 PMCID: PMC9834285 DOI: 10.37349/etat.2022.00113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 12/28/2022] Open
Abstract
The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.
Collapse
Affiliation(s)
- Maurizio Cè
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Maria E. Pellegrino
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Mariachiara Basile
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Adriana Sorce
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | | | - Giancarlo Oliva
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| |
Collapse
|
12
|
Voliotis M, Hanassab S, Abbara A, Heinis T, Dhillo WS, Tsaneva-Atanasova K. Quantitative approaches in clinical reproductive endocrinology. CURRENT OPINION IN ENDOCRINE AND METABOLIC RESEARCH 2022; 27:100421. [PMID: 36643692 PMCID: PMC9831018 DOI: 10.1016/j.coemr.2022.100421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.
Collapse
Key Words
- AI, artificial intelligence
- AMH, anti-Müllerian hormone
- ART, assisted reproductive technology
- Artificial intelligence
- Assisted reproductive technology
- BSA, Bayesian Spectrum Analysis
- Clinical decision making
- E2, estradiol
- FSH, follicle-stimulating hormone
- GnRH, gonadotropin-releasing hormone
- HA, hypothalamic amenorrhea
- HPG, hypothalamic-pituitary gonadal
- IVF, in vitro fertilization
- In vitro fertilization
- LH, luteinizing hormone
- ML, machine learning
- Machine learning
- Mathematical modelling
- OHSS, ovarian hyperstimulation syndrome
- P4, progesterone
- PCOS, polycystic ovary syndrome
- Pulsatility analysis
- Quantitative modelling
- Reproductive endocrinology
Collapse
Affiliation(s)
- Margaritis Voliotis
- Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Simon Hanassab
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom
- Department of Computing, Imperial College London, London, United Kingdom
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom
| | - Ali Abbara
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom
| | - Thomas Heinis
- Department of Computing, Imperial College London, London, United Kingdom
| | - Waljit S. Dhillo
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| |
Collapse
|
13
|
Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
Collapse
|
14
|
Ding L, Bradford C, Kuo IL, Fan Y, Ulin K, Khalifeh A, Yu S, Liu F, Saleeby J, Bushe H, Smith K, Bianciu C, LaRosa S, Prior F, Saltz J, Sharma A, Smyczynski M, Bishop-Jodoin M, Laurie F, Iandoli M, Moni J, Cicchetti MG, FitzGerald TJ. Radiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Science. Front Oncol 2022; 12:931294. [PMID: 36033446 PMCID: PMC9399423 DOI: 10.3389/fonc.2022.931294] [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: 04/28/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
The future of radiation oncology is exceptionally strong as we are increasingly involved in nearly all oncology disease sites due to extraordinary advances in radiation oncology treatment management platforms and improvements in treatment execution. Due to our technology and consistent accuracy, compressed radiation oncology treatment strategies are becoming more commonplace secondary to our ability to successfully treat tumor targets with increased normal tissue avoidance. In many disease sites including the central nervous system, pulmonary parenchyma, liver, and other areas, our service is redefining the standards of care. Targeting of disease has improved due to advances in tumor imaging and application of integrated imaging datasets into sophisticated planning systems which can optimize volume driven plans created by talented personnel. Treatment times have significantly decreased due to volume driven arc therapy and positioning is secured by real time imaging and optical tracking. Normal tissue exclusion has permitted compressed treatment schedules making treatment more convenient for the patient. These changes require additional study to further optimize care. Because data exchange worldwide have evolved through digital platforms and prisms, images and radiation datasets worldwide can be shared/reviewed on a same day basis using established de-identification and anonymization methods. Data storage post-trial completion can co-exist with digital pathomic and radiomic information in a single database coupled with patient specific outcome information and serve to move our translational science forward with nimble query elements and artificial intelligence to ask better questions of the data we collect and collate. This will be important moving forward to validate our process improvements at an enterprise level and support our science. We have to be thorough and complete in our data acquisition processes, however if we remain disciplined in our data management plan, our field can grow further and become more successful generating new standards of care from validated datasets.
Collapse
Affiliation(s)
- Linda Ding
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Carla Bradford
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - I-Lin Kuo
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Yankhua Fan
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Kenneth Ulin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Abdulnasser Khalifeh
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Suhong Yu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fenghong Liu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Jonathan Saleeby
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Harry Bushe
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Koren Smith
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Camelia Bianciu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Salvatore LaRosa
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas, Little Rock, AR, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Mark Smyczynski
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Maryann Bishop-Jodoin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fran Laurie
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Matthew Iandoli
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Janaki Moni
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - M. Giulia Cicchetti
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Thomas J. FitzGerald
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| |
Collapse
|
15
|
Yaqub M, Jinchao F, Arshid K, Ahmed S, Zhang W, Nawaz MZ, Mahmood T. Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8750648. [PMID: 35756423 PMCID: PMC9225884 DOI: 10.1155/2022/8750648] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/12/2022] [Accepted: 05/21/2022] [Indexed: 02/08/2023]
Abstract
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality. In recent years, the literature has focused on deep learning and its applications in medical imaging, particularly image reconstruction. Due to the performance of deep learning models in a wide variety of vision applications, a considerable amount of work has recently been carried out using image reconstruction in medical images. MRI and CT appear as the ultimate scientifically appropriate imaging mode for identifying and diagnosing different diseases in this ascension age of technology. This study demonstrates a number of deep learning image reconstruction approaches and a comprehensive review of the most widely used different databases. We also give the challenges and promising future directions for medical image reconstruction.
Collapse
Affiliation(s)
- Muhammad Yaqub
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Feng Jinchao
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Kaleem Arshid
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Shahzad Ahmed
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Wenqian Zhang
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Muhammad Zubair Nawaz
- College of Science and Shanghai Institute of Intelligent Electronics and Systems, Donghua University, 24105 Songjiang District, Shanghai, China
| | - Tariq Mahmood
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Division of Science and Technology, University of Education, Lahore, Pakistan
| |
Collapse
|
16
|
Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes. Clin Imaging 2022; 87:43-53. [DOI: 10.1016/j.clinimag.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 03/09/2022] [Accepted: 04/11/2022] [Indexed: 11/19/2022]
|
17
|
Su A, Lee H, Tan X, Suarez CJ, Andor N, Nguyen Q, Ji HP. A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precis Oncol 2022; 6:14. [PMID: 35236916 PMCID: PMC8891271 DOI: 10.1038/s41698-022-00252-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 12/16/2021] [Indexed: 12/19/2022] Open
Abstract
Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet.
Collapse
Affiliation(s)
- Andrew Su
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - HoJoon Lee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Xiao Tan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Noemi Andor
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA.
| |
Collapse
|
18
|
Bento M, Fantini I, Park J, Rittner L, Frayne R. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Front Neuroinform 2022; 15:805669. [PMID: 35126080 PMCID: PMC8811356 DOI: 10.3389/fninf.2021.805669] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.
Collapse
Affiliation(s)
- Mariana Bento
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- *Correspondence: Mariana Bento
| | - Irene Fantini
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Justin Park
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Richard Frayne
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| |
Collapse
|
19
|
Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
Collapse
Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
| |
Collapse
|
20
|
Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup Part 2: Annotation, Curation, and Contracting. J Am Coll Radiol 2021; 18:1655-1665. [PMID: 34607753 DOI: 10.1016/j.jacr.2021.07.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/12/2021] [Indexed: 12/29/2022]
Abstract
A core principle of ethical data sharing is maintaining the security and anonymity of the data, and care must be taken to ensure medical records and images cannot be reidentified to be traced back to patients or misconstrued as a breach in the trust between health care providers and patients. Once those principles have been observed, those seeking to share data must take the appropriate steps to curate the data in a way that organizes the clinically relevant information so as to be useful to the data sharing party, assesses the ensuing value of the data set and its annotations, and informs the data sharing contracts that will govern use of the data. Embarking on a data sharing partnership engenders a host of ethical, practical, technical, legal, and commercial challenges that require a thoughtful, considered approach. In 2019 the ACR convened a Data Sharing Workgroup to develop philosophies around best practices in the sharing of health information. This is Part 2 of a Report on the workgroup's efforts in exploring these issues.
Collapse
|
21
|
Sharma A, Tarbox L, Kurc T, Bona J, Smith K, Kathiravelu P, Bremer E, Saltz JH, Prior F. PRISM: A Platform for Imaging in Precision Medicine. JCO Clin Cancer Inform 2021; 4:491-499. [PMID: 32479186 PMCID: PMC7328100 DOI: 10.1200/cci.20.00001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community. METHODS A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA's efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
Collapse
Affiliation(s)
| | - Lawrence Tarbox
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | - Jonathan Bona
- University of Arkansas for Medical Sciences, Little Rock, AR
| | - Kirk Smith
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR
| |
Collapse
|
22
|
Mbugua SN, Njenga LW, Odhiambo RA, Wandiga SO, Onani MO. Beyond DNA-targeting in Cancer Chemotherapy. Emerging Frontiers - A Review. Curr Top Med Chem 2021; 21:28-47. [PMID: 32814532 DOI: 10.2174/1568026620666200819160213] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/07/2020] [Accepted: 07/08/2020] [Indexed: 12/14/2022]
Abstract
Modern anti-cancer drugs target DNA specifically for rapid division of malignant cells. One downside of this approach is that they also target other rapidly dividing healthy cells, such as those involved in hair growth leading to serious toxic side effects and hair loss. Therefore, it would be better to develop novel agents that address cellular signaling mechanisms unique to cancerous cells, and new research is now focussing on such approaches. Although the classical chemotherapy area involving DNA as the set target continues to produce important findings, nevertheless, a distinctly discernible emerging trend is the divergence from the cisplatin operation model that uses the metal as the primary active center of the drug. Many successful anti-cancer drugs present are associated with elevated toxicity levels. Cancers also develop immunity against most therapies and the area of cancer research can, therefore, be seen as an area with a high unaddressed need. Hence, ongoing work into cancer pathogenesis is important to create accurate preclinical tests that can contribute to the development of innovative drugs to manage and treat cancer. Some of the emergent frontiers utilizing different approaches include nanoparticles delivery, use of quantum dots, metal complexes, tumor ablation, magnetic hypothermia and hyperthermia by use of Superparamagnetic Iron oxide Nanostructures, pathomics and radiomics, laser surgery and exosomes. This review summarizes these new approaches in good detail, giving critical views with necessary comparisons. It also delves into what they carry for the future, including their advantages and disadvantages.
Collapse
Affiliation(s)
- Simon N Mbugua
- Department of Chemistry, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
| | - Lydia W Njenga
- Department of Chemistry, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
| | - Ruth A Odhiambo
- Department of Chemistry, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
| | - Shem O Wandiga
- Department of Chemistry, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
| | - Martin O Onani
- Organometallics and Nanomaterials, Department of Chemistry, University of the Western Cape, Private Bag X17, Bellville, 7535, South Africa
| |
Collapse
|
23
|
Saleh M, Bhosale P, Gopireddy DR, Itani M, Galgano S, Morani A. Technologic optimization of a virtual disease focused panel during the COVID pandemic and beyond. Abdom Radiol (NY) 2021; 46:3482-3489. [PMID: 33725146 PMCID: PMC7962634 DOI: 10.1007/s00261-021-03014-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 02/19/2021] [Accepted: 02/25/2021] [Indexed: 11/29/2022]
Abstract
Since the beginning of the COVID-19 pandemic, several service fields have opted for virtual work as much as possible, in order to decrease the spread of the virus. Although several articles have now addressed the key issues in telecommunications and medical education, none have described the digital or virtual functionality of a professional society disease focused panel (DFP) or inter-institutional collaborations. This is extremely important as we believe that the virtual functioning of the DFP will be the new norm for years to come. In this article, we highlight the limitations in the functioning of DFP brought forth due to the ongoing pandemic, while also providing the digital technologic solutions to adapt and also maintain or increase its productivity.
Collapse
Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Priya Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Dheeraj Reddy Gopireddy
- Department of Diagnostic Radiology, The University of Arizona Health Sciences, Tucson, AZ 85721 USA
| | - Malak Itani
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO 63110 USA
| | - Samuel Galgano
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35243 USA
| | - Ajaykumar Morani
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
- Department of Abdominal Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| |
Collapse
|
24
|
Gonzalez JN, Axiotakis LG, Yu VX, Gudis DA, Overdevest JB. Practice of Telehealth in Otolaryngology: A Scoping Review in the Era of COVID-19. Otolaryngol Head Neck Surg 2021; 166:417-424. [PMID: 34003046 DOI: 10.1177/01945998211013751] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE The COVID-19 pandemic has spurred widespread adoption and advancement in telehealth activities, representing a marked change in otolaryngology practice patterns. The present study undertakes a scoping review of research focused on telehealth in otolaryngology (teleotolaryngology) to identify key themes and commonly utilized outcome measures that will assist future development in this growing field. DATA SOURCES PubMed, Embase, and Cochrane databases and reference review. REVIEW METHODS Per guidelines of the PRISMA Extension for Scoping Reviews, we performed database queries using a comprehensive search strategy developed in collaboration with research librarians at the Columbia University Irving Medical Center. We identified 596 unique references to undergo title and abstract review by 2 independent reviewers, leaving 439 studies for full-text review. RESULTS We included 285 studies for extraction of notable findings, leaving 262 unique studies after accounting for content overlap. We identified core outcome measures, including patient and provider satisfaction, costs and benefits, quality of care, feasibility, and access to care. Publication volume increased markedly over time, though only 4% of studies incorporated randomized study group assignment. Using an iterative approach to thematic development, we organized article content across 5 main themes: (1) exploration of teleotolaryngology evolution, (2) role in virtual clinical encounters, (3) applications in interdisciplinary care and educational initiatives, (4) emerging and innovative technologies, and (5) barriers to implementation. CONCLUSION This scoping review of teleotolaryngology documents its evolution and identifies current use cases, limitations, and emerging applications, providing a foundation from which to build future studies, inform policy decision making, and facilitate implementation where appropriate.
Collapse
Affiliation(s)
- Joseph N Gonzalez
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Lucas G Axiotakis
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Victoria X Yu
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.,Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - David A Gudis
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.,Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Jonathan B Overdevest
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.,Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| |
Collapse
|
25
|
Artificial intelligence in child abuse imaging. Pediatr Radiol 2021; 51:1061-1064. [PMID: 33904953 DOI: 10.1007/s00247-021-05073-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 03/08/2021] [Accepted: 03/24/2021] [Indexed: 12/22/2022]
Abstract
There have been rapid advances in artificial intelligence (AI) technology in recent years, and the field of diagnostic imaging is no exception. Just as digital technology revolutionized how radiology is practiced, so these new technologies also appear poised to bring sweeping change. As AI tools make the transition from the theoretical to the everyday, important decisions need to be made about how they will be applied and what their role will be in the practice of radiology. Pediatric radiology presents distinct challenges and opportunities for the application of these tools, and in this article we discuss some of these, specifically as they relate to the prediction, identification and investigation of child abuse.
Collapse
|
26
|
Tizhoosh HR, Fratesi J. COVID-19, AI enthusiasts, and toy datasets: radiology without radiologists. Eur Radiol 2021; 31:3553-3554. [PMID: 33179164 PMCID: PMC7657572 DOI: 10.1007/s00330-020-07453-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 09/23/2020] [Accepted: 11/02/2020] [Indexed: 12/22/2022]
Affiliation(s)
- H R Tizhoosh
- Kimia Lab, University of Waterloo, Waterloo, Canada.
- Vector Institute, MaRS Centre, Toronto, Canada.
| | - Jennifer Fratesi
- Department of Medical Imaging, University Health Network, Toronto, Canada
| |
Collapse
|
27
|
Kang Y, Kim YJ, Park S, Ro G, Hong C, Jang H, Cho S, Hong WJ, Kang DU, Chun J, Lee K, Kang GH, Moon KC, Choe G, Lee KS, Park JH, Jeong WK, Chun SY, Park P, Choi J. Development and operation of a digital platform for sharing pathology image data. BMC Med Inform Decis Mak 2021; 21:114. [PMID: 33812383 PMCID: PMC8019341 DOI: 10.1186/s12911-021-01466-1] [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: 10/20/2020] [Accepted: 03/08/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists. METHODS Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists' workload, AI-assisted annotation was established in collaboration with university AI teams. RESULTS A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. DISCUSSION Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition. CONCLUSIONS Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.
Collapse
Affiliation(s)
- Yunsook Kang
- Department of Biomedical Engineering, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yoo Jung Kim
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seongkeun Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Gun Ro
- Prompt Technology, Co., Ltd., Seoul, Republic of Korea
| | - Choyeon Hong
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyungjoon Jang
- Department of Computer Science, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Sungduk Cho
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Won Jae Hong
- Department of Electrical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Dong Un Kang
- Department of Electrical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jonghoon Chun
- Prompt Technology, Co., Ltd., Seoul, Republic of Korea.,Department of Data Technology, School of Software Convergence, College of ICT Convergence, Myongji University, Seoul, Republic of Korea
| | - Kyoungbun Lee
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Gyeong Hoon Kang
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyoung Chul Moon
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Gheeyoung Choe
- Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Kyu Sang Lee
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jeong Hwan Park
- Department of Pathology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Won-Ki Jeong
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Se Young Chun
- Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, Republic of Korea
| | - Peom Park
- Department of Industrial Engineering, Ajou University, Suwon, Republic of Korea
| | - Jinwook Choi
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea. .,Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
| |
Collapse
|
28
|
Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Phys Med 2021; 83:25-37. [DOI: 10.1016/j.ejmp.2021.02.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/27/2021] [Accepted: 02/15/2021] [Indexed: 02/06/2023] Open
|
29
|
Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F, Michiels S, Souris K, Sterpin E, Lee JA. Artificial intelligence and machine learning for medical imaging: A technology review. Phys Med 2021; 83:242-256. [PMID: 33979715 PMCID: PMC8184621 DOI: 10.1016/j.ejmp.2021.04.016] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/15/2021] [Accepted: 04/18/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
Collapse
Affiliation(s)
- Ana Barragán-Montero
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
| | - Umair Javaid
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Gilmer Valdés
- Department of Radiation Oncology, Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, USA
| | - Paul Desbordes
- Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium
| | - Benoit Macq
- Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium
| | - Siri Willems
- ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium
| | | | | | | | - Steven Michiels
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Kevin Souris
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium; KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Belgium
| | - John A Lee
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| |
Collapse
|
30
|
Mun SK, Wong KH, Lo SCB, Li Y, Bayarsaikhan S. Artificial Intelligence for the Future Radiology Diagnostic Service. Front Mol Biosci 2021; 7:614258. [PMID: 33585563 PMCID: PMC7875875 DOI: 10.3389/fmolb.2020.614258] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/29/2020] [Indexed: 12/18/2022] Open
Abstract
Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.
Collapse
Affiliation(s)
- Seong K. Mun
- Arlington Innovation Center:Health Research, Virginia Tech-Washington DC Area, Arlington, VA, United States
| | | | | | | | | |
Collapse
|
31
|
Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection. Pancreas 2020; 49:882-886. [PMID: 32675784 DOI: 10.1097/mpa.0000000000001603] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Pancreatic cancer continues to be one of the deadliest malignancies and is the third leading cause of cancer-related mortality in the United States. Based on several models, it is projected to become the second leading cause of cancer-related deaths by 2030. Although the overall survival rate for patients diagnosed with pancreatic cancer is less than 10%, survival rates are increasing in those whose cancers are detected at an early stage, when intervention is possible. There are, however, no reliable biomarkers or imaging technology that can detect early-stage pancreatic cancer or accurately identify precursors that are likely to progress to malignancy. The Alliance of Pancreatic Cancer Consortia, a virtual consortium of researchers, clinicians, and advocacies focused on early diagnosis of pancreatic cancer, was formed in 2016 to provide a platform and resources to discover and validate biomarkers and imaging methods for early detection. The focus of discussion at the most recent alliance meeting was on imaging methods and the use of artificial intelligence for early detection of pancreatic cancer.
Collapse
|