1
|
Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
AIMS The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. METHODS An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. RESULTS The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. CONCLUSIONS AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.
Collapse
Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
2
|
Kim KH, Kang HK, Koo HW. Prediction of Intracranial Pressure in Patients with an Aneurysmal Subarachnoid Hemorrhage Using Optic Nerve Sheath Diameter via Explainable Predictive Modeling. J Clin Med 2024; 13:2107. [PMID: 38610872 PMCID: PMC11012720 DOI: 10.3390/jcm13072107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Background: The objective of this investigation was to formulate a model for predicting intracranial pressure (ICP) by utilizing optic nerve sheath diameter (ONSD) during endovascular treatment for an aneurysmal subarachnoid hemorrhage (aSAH), incorporating explainable predictive modeling. Methods: ONSD measurements were conducted using a handheld ultrasonography device during the course of endovascular treatment (n = 126, mean age 58.82 ± 14.86 years, and female ratio 67.46%). The optimal ONSD threshold associated with an increased ICP was determined. Additionally, the association between ONSD and ICP was validated through the application of a linear regression machine learning model. The correlation between ICP and various factors was explored through the modeling. Results: With an ICP threshold set at 20 cmH2O, 82 patients manifested an increased ICP, with a corresponding ONSD of 0.545 ± 0.08 cm. Similarly, with an ICP threshold set at 25 cmH2O, 44 patients demonstrated an increased ICP, with a cutoff ONSD of 0.553 cm. Conclusions: We revealed a robust correlation between ICP and ONSD. ONSD exhibited a significant association and demonstrated potential as a predictor of ICP in patients with an ICP ≥ 25 cmH2O. The findings suggest its potential as a valuable index in clinical practice, proposing a reference value of ONSD for increased ICP in the institution.
Collapse
Affiliation(s)
- Kwang Hyeon Kim
- Clinical Research Support Center, Inje University Ilsan Paik Hospital, Goyang 10380, Republic of Korea
| | - Hyung Koo Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang 10380, Republic of Korea
| | - Hae-Won Koo
- Department of Neurosurgery, College of Medicine, Inje University Ilsan Paik Hospital, Goyang 10380, Republic of Korea
| |
Collapse
|
3
|
Xie Y, Seth I, Hunter-Smith DJ, Rozen WM, Seifman MA. Investigating the impact of innovative AI chatbot on post-pandemic medical education and clinical assistance: a comprehensive analysis. ANZ J Surg 2024; 94:68-77. [PMID: 37602755 DOI: 10.1111/ans.18666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND The COVID-19 pandemic has significantly disrupted clinical experience and exposure of medical students and junior doctors. Artificial Intelligence (AI) integration in medical education has the potential to enhance learning and improve patient care. This study aimed to evaluate the effectiveness of three popular large language models (LLMs) in serving as clinical decision-making support tools for junior doctors. METHODS A series of increasingly complex clinical scenarios were presented to ChatGPT, Google's Bard and Bing's AI. Their responses were evaluated against standard guidelines, and for reliability by the Flesch Reading Ease Score, Flesch-Kincaid Grade Level, the Coleman-Liau Index, and the modified DISCERN score for assessing suitability. Lastly, the LLMs outputs were assessed by using the Likert scale for accuracy, informativeness, and accessibility by three experienced specialists. RESULTS In terms of readability and reliability, ChatGPT stood out among the three LLMs, recording the highest scores in Flesch Reading Ease (31.2 ± 3.5), Flesch-Kincaid Grade Level (13.5 ± 0.7), Coleman-Lau Index (13) and DISCERN (62 ± 4.4). These results suggest statistically significant superior comprehensibility and alignment with clinical guidelines in the medical advice given by ChatGPT. Bard followed closely behind, with BingAI trailing in all categories. The only non-significant statistical differences (P > 0.05) were found between ChatGPT and Bard's readability indices, and between the Flesch Reading Ease scores of ChatGPT/Bard and BingAI. CONCLUSION This study demonstrates the potential utility of LLMs in fostering self-directed and personalized learning, as well as bolstering clinical decision-making support for junior doctors. However further development is needed for its integration into education.
Collapse
Affiliation(s)
- Yi Xie
- Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia
| | - Ishith Seth
- Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, Australia
| | - David J Hunter-Smith
- Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, Australia
| | - Warren M Rozen
- Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, Australia
| | - Marc A Seifman
- Department of Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Faculty of Science, Medicine, and Health, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
4
|
Pacchiano F, Tortora M, Criscuolo S, Jaber K, Acierno P, De Simone M, Tortora F, Briganti F, Caranci F. Artificial intelligence applied in acute ischemic stroke: from child to elderly. LA RADIOLOGIA MEDICA 2024; 129:83-92. [PMID: 37878222 PMCID: PMC10808481 DOI: 10.1007/s11547-023-01735-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/28/2023] [Indexed: 10/26/2023]
Abstract
This review will summarize artificial intelligence developments in acute ischemic stroke in recent years and forecasts for the future. Stroke is a major healthcare concern due to its effects on the patient's quality of life and its dependence on the timing of the identification as well as the treatment. In recent years, attention increased on the use of artificial intelligence (AI) systems to help categorize, prognosis, and to channel these patients toward the right therapeutic procedure. Machine learning (ML) and in particular deep learning (DL) systems using convoluted neural networks (CNN) are becoming increasingly popular. Various studies over the years evaluated the use of these methods of analysis and prediction in the assessment of stroke patients, and at the same time, several applications and software have been developed to support the neuroradiologists and the stroke team to improve patient outcomes.
Collapse
Affiliation(s)
- Francesco Pacchiano
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Mario Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 5, 80131, Naples, Italy.
| | - Sabrina Criscuolo
- Pediatric University Department, Bambino Gesù Children Hospital, Rome, Italy
| | - Katya Jaber
- Department of Elektrotechnik und Informatik, Hochschule Bremen, Bremen, Germany
| | | | - Marta De Simone
- UOC Neuroradiology, AORN San Giuseppe Moscati, Avellino, Italy
| | - Fabio Tortora
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 5, 80131, Naples, Italy
| | - Francesco Briganti
- Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini, 5, 80131, Naples, Italy
| | - Ferdinando Caranci
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Caserta, Italy
| |
Collapse
|
5
|
Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc 2023; 30:2050-2063. [PMID: 37647865 PMCID: PMC10654852 DOI: 10.1093/jamia/ocad180] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings. MATERIALS AND METHODS We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized. RESULTS Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed. CONCLUSION ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.
Collapse
Affiliation(s)
- Anindya Pradipta Susanto
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
| | - David Lyell
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Bambang Widyantoro
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
- National Cardiovascular Center Harapan Kita Hospital, Jakarta, DKI Jakarta 11420, Indonesia
| | - Shlomo Berkovsky
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| |
Collapse
|
6
|
Dang J, Lal A, Montgomery A, Flurin L, Litell J, Gajic O, Rabinstein A. Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit. BMC Neurol 2023; 23:161. [PMID: 37085850 PMCID: PMC10121414 DOI: 10.1186/s12883-023-03192-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/30/2023] [Indexed: 04/23/2023] Open
Abstract
INTRODUCTION Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group's existing digital twin model for the treatment of sepsis. METHODS The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 ("agree") or 7 ("strongly agree"). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model. RESULTS After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements. CONCLUSION This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.
Collapse
Affiliation(s)
- Johnny Dang
- Department of Neurology, Cleveland Clinic, Cleveland, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA.
| | | | - Laure Flurin
- Infectious Diseases Research Laboratory, Mayo Clinic, Rochester, USA
- Department of Critical Care, University Hospital of Guadeloupe, Guadeloupe, France
| | - John Litell
- Abbott Northwestern Emergency Critical Care, Minneapolis, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA
| | | |
Collapse
|
7
|
Cui L, Fan Z, Yang Y, Liu R, Wang D, Feng Y, Lu J, Fan Y. Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2456550. [PMID: 36420096 PMCID: PMC9678444 DOI: 10.1155/2022/2456550] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/27/2022] [Accepted: 10/20/2022] [Indexed: 09/15/2023]
Abstract
Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.
Collapse
Affiliation(s)
- Liyuan Cui
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhiyuan Fan
- Centre of Intelligent Medical Technology and Equipment, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Yingjian Yang
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Rui Liu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dajiang Wang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yingying Feng
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Jiahui Lu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yifeng Fan
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| |
Collapse
|
8
|
Huang B, Tan G, Dou H, Cui Z, Song Y, Zhou T. Mutual gain adaptive network for segmenting brain stroke lesions. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
9
|
Heo S, Ha J, Jung W, Yoo S, Song Y, Kim T, Cha WC. Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury. Sci Rep 2022; 12:12454. [PMID: 35864281 PMCID: PMC9304372 DOI: 10.1038/s41598-022-16313-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/07/2022] [Indexed: 11/09/2022] Open
Abstract
The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety.
Collapse
Affiliation(s)
- Sejin Heo
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Juhyung Ha
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA
| | - Weon Jung
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Suyoung Yoo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeejun Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Taerim Kim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
| |
Collapse
|
10
|
Liew SL, Lo BP, Donnelly MR, Zavaliangos-Petropulu A, Jeong JN, Barisano G, Hutton A, Simon JP, Juliano JM, Suri A, Wang Z, Abdullah A, Kim J, Ard T, Banaj N, Borich MR, Boyd LA, Brodtmann A, Buetefisch CM, Cao L, Cassidy JM, Ciullo V, Conforto AB, Cramer SC, Dacosta-Aguayo R, de la Rosa E, Domin M, Dula AN, Feng W, Franco AR, Geranmayeh F, Gramfort A, Gregory CM, Hanlon CA, Hordacre BG, Kautz SA, Khlif MS, Kim H, Kirschke JS, Liu J, Lotze M, MacIntosh BJ, Mataró M, Mohamed FB, Nordvik JE, Park G, Pienta A, Piras F, Redman SM, Revill KP, Reyes M, Robertson AD, Seo NJ, Soekadar SR, Spalletta G, Sweet A, Telenczuk M, Thielman G, Westlye LT, Winstein CJ, Wittenberg GF, Wong KA, Yu C. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Sci Data 2022; 9:320. [PMID: 35710678 PMCID: PMC9203460 DOI: 10.1038/s41597-022-01401-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/19/2022] [Indexed: 01/16/2023] Open
Abstract
Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.
Collapse
Affiliation(s)
- Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Bethany P Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Miranda R Donnelly
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Artemis Zavaliangos-Petropulu
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica N Jeong
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Giuseppe Barisano
- Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Alexandre Hutton
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Julia P Simon
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Julia M Juliano
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Anisha Suri
- Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhizhuo Wang
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Aisha Abdullah
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Jun Kim
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Ard
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Michael R Borich
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lara A Boyd
- Department of Physical Therapy & Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Amy Brodtmann
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Cathrin M Buetefisch
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Lei Cao
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Jessica M Cassidy
- Department of Allied Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Adriana B Conforto
- Hospital das Clínicas, São Paulo University, Sao Paulo, SP, Brazil
- Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil
| | - Steven C Cramer
- Department of Neurology, University of California Los Angeles and California Rehabilitation Institute, Los Angeles, CA, USA
| | - Rosalia Dacosta-Aguayo
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
| | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium
- Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Adrienne N Dula
- Departments of Neurology and Diagnostic Medicine, Dell Medical School at The University of Texas Austin, Austin, TX, USA
| | - Wuwei Feng
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Alexandre Gramfort
- Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France
| | - Chris M Gregory
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
| | - Colleen A Hanlon
- Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Brenton G Hordacre
- Innovation, Implementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Steven A Kautz
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
- Ralph H Johnson VA Medical Center, Charleston, SC, USA
| | - Mohamed Salah Khlif
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia
| | - Hosung Kim
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jan S Kirschke
- Neuroradiology, School of Medicine, Technical University Munich, München, Germany
| | - Jingchun Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Martin Lotze
- Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Hurvitz Brain Sciences Program, Toronto, Ontario, Canada
| | - Maria Mataró
- Department of Clinical Psychology and Psychobiology, Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, 08950, Esplugues de Llobregat, Spain
| | - Feroze B Mohamed
- Jefferson Magnetic Resonance Imaging Center, Philadelphia, PA, USA
| | - Jan E Nordvik
- CatoSenteret Rehabilitation Center, SON, Norway
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Gilsoon Park
- Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Amy Pienta
- Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Shane M Redman
- Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Kate P Revill
- Facility for Education and Research in Neuroscience, Emory University, Atlanta, GA, USA
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Andrew D Robertson
- Schlegel-University of Waterloo Research Institute for Aging, University of Waterloo, Waterloo, Ontario, Canada
- Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Na Jin Seo
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
- Ralph H Johnson VA Medical Center, Charleston, SC, USA
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Neurosciences (CCM), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Division of Neuropsychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Alison Sweet
- Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Maria Telenczuk
- Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France
| | - Gregory Thielman
- Department of Physical Therapy and Neuroscience, Samson College of Health Sciences, St. Joseph's University, Philadelphia, PA, USA
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Carolee J Winstein
- Division of Biokinesiology and Physical Therapy of the Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - George F Wittenberg
- Geriatrics Research, Education and Clinical Center, HERL, Department of Veterans Affairs, Pittsburgh, PA, USA
- Departments of Neurology, PM&R, RNEL, CNBC, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristin A Wong
- Department of Physical Medicine & Rehabilitation, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| |
Collapse
|
11
|
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Cassinelli Petersen GI, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers (Basel) 2022; 14:cancers14112623. [PMID: 35681603 PMCID: PMC9179416 DOI: 10.3390/cancers14112623] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 01/27/2023] Open
Abstract
Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.
Collapse
Affiliation(s)
- Sara Merkaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Department of Neurosurgery, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ryan C. Bahar
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Visage Imaging, Inc., 12625 High Bluff Dr, Suite 205, San Diego, CA 92130, USA
| | - Ichiro Ikuta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | | | - Gabriel I. Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Lawrence Staib
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
| | - John T. Mongan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA; (J.T.M.); (S.C.)
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave., San Francisco, CA 94143, USA; (J.T.M.); (S.C.)
| | - Mariam S. Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06520, USA; (S.M.); (R.C.B.); (T.Z.); (M.L.); (I.I.); (G.I.C.P.); (L.S.); (S.P.)
- Correspondence: ; Tel.: +650-285-7577
| |
Collapse
|