检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Tanno, Ryutaro Worrall, Daniel E. Kaden, Enrico Ghosh, Aurobrata Grussu, Francesco Bizzi, Alberto Sotiropoulos, Stamatios N. Criminisi, Antonio Alexander, Daniel C. Centre for Medical Image Computing and Dept. Computer Science Ucl Gower Street LondonWC1E 6BT United Kingdom Machine Learning Lab University of Amsterdam Netherlands Faculty of Brain Sciences Institute of Neurology Ucl United Kingdom Neuroradiology Unit Foundation Irccs Carlo Besta Neurological Institute Milan Italy School of Medicine and Nihr Biomedical Research Centre Sir Peter Mansfield Imaging Centre University of Nottingham United Kingdom Wellcome Centre for Integrative Neuroimaging University of Oxford United Kingdom Microsoft Research Cambridge United Kingdom

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date little consideration has been given to uncertainty quantification over the output image. Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we propose to account for intrinsic uncertainty through a heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference, and integrate the two to quantify predictive uncertainty over the output image. Moreover, we introduce a method to propagate the predictive uncertainty on a multi-channelled image to derived scalar parameters, and separately quantify the effects of intrinsic and parameter uncertainty therein. The methods are evaluated for super-resolution of two different signal representations of diffusion MR images-Diffusion Tensor images and Mean Apparent Propagator MRI-and their derived quantities such as mean diffusivity and fractional anisotropy, on multiple datasets of both healthy and pathological human brains. Results highlight three key potential benefits of uncertainty modelling for improving the safety of DL-based image enhancement systems. Firstly, incorporating uncertainty modelling improves the predictive performance even when test data departs from training data. Secondly, the predictive uncertainty highly correlates with reconstruction errors, and is therefore capable of detecting predictive "failures". Results on both healthy subjects and patients with brain glioma or multiple sclerosis demonstrate that such an uncertainty measure enables subject-specific and voxel-wise risk assessment of the super-resolved images that can be accounted for in subsequent analysis. Thirdly, we show that the method for decomposing predictive uncertainty into its independent sources provides high-level "explanations" for the model p

关键词： Optical resolving power

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Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Medicine Plus

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Medicine Plus 2024年第2期1卷 102-124页

作者： Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin Shen Bin Dong Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and Institute Beijing 100142China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking University Beijing 100871China National Biomedical Imaging Center Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Massachusetts General Hospital Boston MA 02114-2696USA Harvard Medical School BostonMA 02115USA

With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and *** has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision ***,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating *** review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic *** the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic *** address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for ***,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical *** have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

关键词： Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning

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LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

arXiv

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arXiv 2024年

作者： Dorent, Reuben Khajavi, Roya Idris, Tagwa Ziegler, Erik Somarouthu, Bhanusupriya Jacene, Heather LaCasce, Ann Deissler, Jonathan Ehrhardt, Jan Engelson, Sofija Fischer, Stefan M. Gu, Yun Handels, Heinz Kasai, Satoshi Kondo, Satoshi Maier-Hein, Klaus Schnabel, Julia A. Wang, Guotai Wang, Litingyu Wald, Tassilo Yang, Guang-Zhong Zhang, Hanxiao Zhang, Minghui Pieper, Steve Harris, Gordon Kikinis, Ron Kapur, Tina Brigham and Women’s Hospital Harvard Medical School BostonMA United States Massachusetts General Hospital Harvard Medical School BostonMA United States Yunu Inc. CaryNC United States Isomics Inc CambridgeMA United States Dana-Farber Cancer Institute BostonMA United States Technical University Munich Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Institute of Medical Informatics University of Lübeck Lübeck Germany German Research Center for Artificial Intelligence Lübeck Germany Niigata University of Health and Welfare Niigata Japan Muroran Institute of Technology Hokkaido Japan Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Electronic Science and Technology of China Chengdu China Heidelberg Germany University of Heidelberg Heidelberg Germany Shanghai AI laboratory Shanghai China

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of 61.0%. On the other hand, top-ranked teams, with a median Dice score exceeding 70%, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance. . . © 2024, CC BY.

关键词： Image segmentation

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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

arXiv

引用

arXiv 2019年

作者： Kuijf, Hugo J. Biesbroek, J. Matthijs Bresser, Jeroen de Heinen, Rutger Andermatt, Simon Bento, Mariana Berseth, Matt Belyaev, Mikhail Cardoso, M. Jorge Casamitjana, Adrià Collins, D. Louis Dadar, Mahsa Georgiou, Achilleas Ghafoorian, Mohsen Jin, Dakai Khademi, April Knight, Jesse Li, Hongwei Lladó, Xavier Luna, Miguel Mahmood, Qaiser McKinley, Richard Mehrtash, Alireza Ourselin, Sébastien Park, Bo-yong Park, Hyunjin Park, Sang Hyun Pezold, Simon Puybareau, Elodie Rittner, Leticia Sudre, Carole H. Valverde, Sergi Vilaplana, Verónica Wiest, Roland Xu, Yongchao Xu, Ziyue Zeng, Guodong Zhang, Jianguo Zheng, Guoyan Chen, Christopher Flier, Wiesje van der Barkhof, Frederik Viergever, Max A. Biessels, Geert Jan Image Sciences Institute UMC Utrecht Utrecht University Netherlands Brain Center Rudolf Magnus UMC Utrecht Utrecht University Netherlands Department of Radiology UMC Utrecht Department of Radiology LUMC Leiden Netherlands Department of Biomedical Engineering University of Basel Allschwil Switzerland Radiology and Clinical Neuroscience Hotchkiss Brain Institute University of Calgary AB Canada NLP Logix Skolkovo Institute of Science and Technology School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical Image Computing University College London Signal Theory and Communications Department Universitat Politècnica de Catalunya BarcelonaTech Barcelona Spain McGill University Canada Computational Statistics and Machine Learning MSc University College London TomTom Amsterdam Netherlands Department of Radiology and Imaging Science National Institutes of Health United States Ryerson University Canada University of Guelph Canada Sun Yat-sen University University of Dundee Technical University of Munich Research institute of Computer Vision and Robotics University of Girona Spain Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Pakistan Institute of Nuclear Science and Technology Support Center for Advanced Neuroimaging Institute for Diagnostic and Interventional Neuroradiology Inselspital University of Bern Switzerland Electrical and Computer Engineering Department University of British Columbia Vancouver Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston School of Biomedical Engineering and Imaging Sciences King’s College London Suwon Korea Republic of School of Electronic and Electrical Engineering Sungkyunkwan University Suwon Korea Republic of France School of Electrical and Computer Engineering University of Campinas SP Brazil School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical I

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://***/).Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner *** participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen *** challenge remains open for future submissions and provides a public platform for method evaluation. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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GHOSTBUSTER: A PHASE RETRIEVAL DIFFRACTION TOMOGRAPHY ALGORITHM FOR CRYO-EM

arXiv

引用

arXiv 2023年

作者： Yeo, Joel Daurer, Benedikt J. Kimanius, Dari Balakrishnan, Deepan Bepler, Tristan Tan, Yong Zi Duane Loh, N. NUS Graduate School for Integrative Sciences and Engineering Programme National University of Singapore Singapore119077 Singapore Department of Physics National University of Singapore Singapore117551 Singapore 2 Fusionopolis Way Innovis #08-03 Singapore138634 Singapore Department of Biological Sciences National University of Singapore Singapore117558 Singapore Center for Bio-Imaging Sciences National University of Singapore Singapore117557 Singapore Diamond Light Source Harwell Campus DidcotOX11 0DE United Kingdom MRC Laboratory of Molecular Biology Francis Crick Avenue CambridgeCB2 0QH United Kingdom CZ Imaging Institute 3400 Bridge Parkway Redwood CityCA94065 United States Simons Machine Learning Center New York Structural Biology Center New YorkNY United States 8A Biomedical Grove Singapore138648 Singapore 61 Biopolis Drive Proteos Singapore138673 Singapore

Ewald sphere curvature correction, which extends beyond the projection approximation, stretches the shallow depth of field in cryo-EM reconstructions of thick particles. Here we show that even for previously assumed thin particles, reconstruction artifacts which we refer to as ghosts can appear. By retrieving the lost phases of the electron exitwaves and accounting for the first Born approximation scattering within the particle, we show that these ghosts can be effectively eliminated. Our simulations demonstrate how such ghostbusting can improve reconstructions as compared to existing state-of-the-art software. Like ptychographic cryo-EM, our Ghostbuster algorithm uses phase retrieval to improve reconstructions, but unlike the former, we do not need to modify the existing data acquisition pipelines. © 2023, CC BY-NC-SA.

关键词： Data acquisition

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

引用

International Conference on Laser Optics

作者： L. M. Oliveira T. M. Gonç alves A. R. Botelho I. S. Martins H. F. Silva I. Carneiro S. Carvalho R. Henrique V. V. Tuchin ISEP Center of Innovation in Engineering and Industrial Technology Porto Portugal Physics Department Polytechnic of Porto - School of Engineering (ISEP) Porto Portugal Department of Electrical and Computer Engineering Porto University - School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Dept. of Pathological Cytological and Thanatological Anatomy Polytechnic of Porto - School of Health (ESS) Porto Portugal Deptartment of Pathology Santa Luzia Hospital (ULSAM) Viana do Castelo Portugal Dept. of Pat hol. and Molecular Immunology Porto University - Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control FRC &#x2018 Saratov Scientific Centre&#x2019 of the Russian Academy of Sciences Saratov Russia

ISBN: (数字)9781665466646

ISBN: (纸本)9781665466653

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.

关键词： Rabbits Absorption Neurons Lasers Aging Pigments Optics

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A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

arXiv

引用

arXiv 2024年

作者： Garrucho, Lidia Kushibar, Kaisar Reidel, Claire-Anne Joshi, Smriti Osuala, Richard Tsirikoglou, Apostolia Bobowicz, Maciej del Riego, Javier Catanese, Alessandro Gwoździewicz, Katarzyna Cosaka, Maria-Laura Abo-Elhoda, Pasant M. Tantawy, Sara W. Sakrana, Shorouq S. Shawky-Abdelfatah, Norhan O. Abdo-Salem, Amr Muhammad Kozana, Androniki Divjak, Eugen Ivanac, Gordana Nikiforaki, Katerina Klontzas, Michail E. García-Dosdá, Rosa Gulsun-Akpinar, Meltem Lafcı, Oğuz Mann, Ritse Martín-Isla, Carlos Prior, Fred Marias, Kostas Starmans, Martijn P.A. Strand, Fredrik Díaz, Oliver Igual, Laura Lekadir, Karim Barcelona Spain Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany 2nd Dept. of Radiology Medical University of Gdansk Gdansk Poland Parc Taulí Hospital Universitari Sabadell Spain Hospital Germans Trias i Pujol Badalona Spain Centro Mamario Instituto Alexander Fleming Buenos Aires Argentina Department of Diagnostic & Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo Egypt Department of Radiology University Hospital of Heraklion Stavrakia Greece Department of Diagnostic and Interventional Radiology University Hospital Dubrava Zagreb Croatia University of Zagreb School of Medicine Zagreb Croatia Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research and Technology—Hellas Heraklion Greece Department of Radiology School of Medicine University of Crete Heraklion Greece Medical Imaging and Radiology Universitary and Politechnic Hospital La Fe Valencia Spain Department of Radiology Hacettepe University Faculty of Medicine Sihhiye Ankara Turkey Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria Department of Radiology and Nuclear Medicine Radboud University Medical Center Netherlands University of Arkansas for Medical Sciences Little RockAR United States Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Heraklion Greece Department of Radiology and Nuclear Medicine Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Department of Pathology Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Breast Radiology Karolinska University Hospital Stockholm Sweden Passeig Lluís Companys 23 Barcelona Sp

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pretreatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

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ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics

arXiv

引用

arXiv 2024年

作者： Banerjee, Oishi Saenz, Agustina Wu, Kay Clements, Warren Zia, Adil Buensalido, Dominic Kavnoudias, Helen Abi-Ghanem, Alain S. El Ghawi, Nour Luna, Cibele Castillo, Patricia Al-Surimi, Khaled Daghistani, Rayyan A. Chen, Yuh-Min Chao, Heng-Sheng Heiliger, Lars Kim, Moon Haubold, Johannes Jonske, Frederic Rajpurkar, Pranav Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Radiology Alfred Health MelbourneVIC Australia Department of Diagnostic Radiology American University of Beirut Beirut Lebanon Department of Radiology University of Miami Miller School of Medicine MiamiFL United States University of Miami Jackson Memorial Hospital MiamiFL United States Department of Healthcare Management University of Doha for Science and Technology Doha Qatar Department of Medical Imaging King Abdulaziz Medical City Riyadh Saudi Arabia Department of Chest Medicine Taipei Veterans General Hospital Taipei Taiwan Institute for AI in Medicine University Hospital Essen North Rhine-Westphalia Essen Germany Department of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen North Rhine-Westphalia Essen Germany Department of Medical Machine Learning Institute of AI in Medicine University Medicine Essen North Rhine-Westphalia Essen Germany MAIDA Initiative Partners

Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology reports across diverse hospitals. We develop ReXamine-Global, a LLM-powered, multi-site framework that tests metrics across different writing styles and patient populations, exposing gaps in their generalization. First, our method tests whether a metric is undesirably sensitive to reporting style, providing different scores depending on whether AI-generated reports are stylistically similar to ground-truth reports or not. Second, our method measures whether a metric reliably agrees with experts, or whether metric and expert scores of AI-generated report quality diverge for some sites. Using 240 reports from 6 hospitals around the world, we apply ReXamine-Global to 7 established report evaluation metrics and uncover serious gaps in their generalizability. Developers can apply ReXamine-Global when designing new report evaluation metrics, ensuring their robustness across sites. Additionally, our analysis of existing metrics can guide users of those metrics towards evaluation procedures that work reliably at their sites of interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Population statistics

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Confidence intervals uncovered: Are we ready for real-world medical imaging AI?

arXiv

引用

arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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