1
40
6
-
Hyperlink
A link, or reference, to another resource on the Internet.
URL
https://doi.org/10.1016/j.ejca.2022.09.018
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Title
A name given to the resource
Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis
Creator
An entity primarily responsible for making the resource
André Pfob
Chris Sidey-Gibbons
Richard G Barr
Volker Duda
Zaher Alwafai
Corinne Balleyguier
Dirk-André Clevert
Sarah Fastner
Christina Gomez
Manuela Goncalo
Ines Gruber
Markus Hahn
André Hennigs
Panagiotis Kapetas
Sheng-Chieh Lu
Juliane Nees
Ralf Ohlinger
Fabian Riedel
Matthieu Rutten
Benedikt Schaefgen
Anne Stieber
Riku Togawa
Mitsuhiro Tozaki
Sebastian Wojcinski
Cai Xu
Geraldine Rauch
Joerg Heil
Michael Golatta
Date
A point or period of time associated with an event in the lifecycle of the resource
2022
Description
An account of the resource
Background: Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.
Methods: We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE.
Results: In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound.
Conclusion: The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.
Source
A related resource from which the described resource is derived
Eur J Cancer
. 2022 Dec;177:1-14. doi: 10.1016/j.ejca.2022.09.018. Epub 2022 Sep 28.
Language
A language of the resource
English
2022
Artificial Intelligence
breast cancer
Breast imaging
elastography
Machine learning
-
Hyperlink
A link, or reference, to another resource on the Internet.
URL
https://doi.org/10.1007/s00330-021-08519-z
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis
Creator
An entity primarily responsible for making the resource
André Pfob
Chris Sidey-Gibbons
Richard G Barr
Volker Duda
Zaher Alwafai
Corinne Balleyguier
Dirk-André Clevert
Sarah Fastner
Christina Gomez
Manuela Goncalo
Ines Gruber
Markus Hahn
André Hennigs
Panagiotis Kapetas
Sheng-Chieh Lu
Juliane Nees
Ralf Ohlinger
Fabian Riedel
Matthieu Rutten
Benedikt Schaefgen
Maximilian Schuessler
Anne Stieber
Riku Togawa
Mitsuhiro Tozaki
Sebastian Wojcinski
Cai Xu
Geraldine Rauch
Joerg Heil
Michael Golatta
Date
A point or period of time associated with an event in the lifecycle of the resource
2022
Description
An account of the resource
Objectives: AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms.
Methods: Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC).
Results: Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05).
Conclusions: The performance of humans and AI-based algorithms improves with multi-modal information.
Key points: • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses.
Source
A related resource from which the described resource is derived
Eur Radiol
. 2022 Jun;32(6):4101-4115. doi: 10.1007/s00330-021-08519-z. Epub 2022 Feb 17.
Language
A language of the resource
English
2022
Artificial Intelligence
breast cancer
Machine learning
Ultrasonography
-
Hyperlink
A link, or reference, to another resource on the Internet.
URL
https://doi.org/10.1016/j.jemermed.2022.01.001
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Artificial Intelligence in Emergency Medicine: Benefits, Risks, and Recommendations
Creator
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Laura Vearrier
Arthur R Derse
Jesse B Basford
Gregory Luke Larkin
John C Moskop
Date
A point or period of time associated with an event in the lifecycle of the resource
2022
Description
An account of the resource
Background: Artificial intelligence (AI) can be described as the use of computers to perform tasks that formerly required human cognition. The American Medical Association prefers the term 'augmented intelligence' over 'artificial intelligence' to emphasize the assistive role of computers in enhancing physician skills as opposed to replacing them. The integration of AI into emergency medicine, and clinical practice at large, has increased in recent years, and that trend is likely to continue.
Discussion: AI has demonstrated substantial potential benefit for physicians and patients. These benefits are transforming the therapeutic relationship from the traditional physician-patient dyad into a triadic doctor-patient-machine relationship. New AI technologies, however, require careful vetting, legal standards, patient safeguards, and provider education. Emergency physicians (EPs) should recognize the limits and risks of AI as well as its potential benefits.
Conclusions: EPs must learn to partner with, not capitulate to, AI. AI has proven to be superior to, or on a par with, certain physician skills, such as interpreting radiographs and making diagnoses based on visual cues, such as skin cancer. AI can provide cognitive assistance, but EPs must interpret AI results within the clinical context of individual patients. They must also advocate for patient confidentiality, professional liability coverage, and the essential role of specialty-trained EPs.
Source
A related resource from which the described resource is derived
J Emerg Med
. 2022 Apr;62(4):492-499. doi: 10.1016/j.jemermed.2022.01.001. Epub 2022 Feb 11.
Language
A language of the resource
English
2022
Artificial Intelligence
big data
Bioethics
Emergency Medicine
Humanism
information technology
Machine learning
physician-patient relations.
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/jep.13042</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
1293-1309
Issue
6
Volume
24
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Exploring comorbid depression and physical health trajectories: A case-based computational modelling approach.
Publisher
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Journal of evaluation in clinical practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-12
Subject
The topic of the resource
Adult; Female; Humans; Male; Middle Aged; Socioeconomic Factors; Aged; Chronic Disease; *Computer Simulation; Longitudinal Studies; Comorbidity; *Health Status; Artificial Intelligence; cluster analysis; Life Change Events; primary care; Artificial Intelligence; case-based modelling; comorbid depression and physical health; complexity theory; differential equations; longitudinal analysis; nonlinear dynamics; Systems Analysis; Adult Survivors of Child Abuse/statistics & numerical data; Depression/*epidemiology/*physiopathology; Health Services Research/*methods; Intimate Partner Violence/statistics & numerical data; Primary Health Care/organization & administration
Creator
An entity primarily responsible for making the resource
Castellani Brian; Griffiths Frances; Rajaram Rajeev; Gunn Jane
Description
An account of the resource
While comorbid depression/physical health is a major clinical concern, the conventional methods of medicine make it difficult to model the complexities of this relationship. Such challenges include cataloguing multiple trends, developing multiple complex aetiological explanations, and modelling the collective large-scale dynamics of these trends. Using a case-based complexity approach, this study engaged in a richly described case study to demonstrate the utility of computational modelling for primary care research. N = 259 people were subsampled from the Diamond database, one of the largest primary care depression cohort studies worldwide. A global measure of depressive symptoms (PHQ-9) and physical health (PCS-12) were assessed at 3, 6, 9, and 12 months and then annually for a total of 7 years. Eleven trajectories and 2 large-scale collective dynamics were identified, revealing that while depression is comorbid with poor physical health, chronic illness is often low dynamic and not always linked to depression. Also, some of the cases in the unhealthy and oscillator trends remain ill without much chance of improvement. Finally, childhood abuse, partner violence, and negative life events are greater amongst unhealthy trends. Computational modelling offers a major advance for health researchers to account for the diversity of primary care patients and for developing better prognostic models for team-based interdisciplinary care.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">10.1111/jep.13042</a>
*Computer Simulation
*Health Status
2018
Adult
Adult Survivors of Child Abuse/statistics & numerical data
Aged
Artificial Intelligence
case-based modelling
Castellani Brian
Chronic Disease
Cluster Analysis
comorbid depression and physical health
Comorbidity
complexity theory
Depression/*epidemiology/*physiopathology
differential equations
Female
Griffiths Frances
Gunn Jane
Health Services Research/*methods
Humans
Intimate Partner Violence/statistics & numerical data
Journal of evaluation in clinical practice
Life Change Events
longitudinal analysis
Longitudinal Studies
Male
Middle Aged
Nonlinear Dynamics
primary care
Primary Health Care/organization & administration
Rajaram Rajeev
Socioeconomic Factors
Systems Analysis
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1155/2013/873595" target="_blank" rel="noreferrer noopener">http://doi.org/10.1155/2013/873595</a>
Pages
873595–873595
Volume
2013
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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A gradient boosting algorithm for survival analysis via direct optimization of concordance index.
Publisher
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Computational and mathematical methods in medicine
Date
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2013
1905-07
Subject
The topic of the resource
*Survival Analysis; Algorithms; Artificial Intelligence; Breast Neoplasms/*epidemiology/*mortality; Clinical; Databases; Decision Support Systems; Factual; Female; Humans; Internet; Models; Prognosis; Proportional Hazards Models; Software; Theoretical
Creator
An entity primarily responsible for making the resource
Chen Yifei; Jia Zhenyu; Mercola Dan; Xie Xiaohui
Description
An account of the resource
Survival analysis focuses on modeling and predicting the time to an event of interest. Many statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index) and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1155/2013/873595" target="_blank" rel="noreferrer noopener">10.1155/2013/873595</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
*Survival Analysis
2013
Algorithms
Artificial Intelligence
Breast Neoplasms/*epidemiology/*mortality
Chen Yifei
Clinical
Computational and mathematical methods in medicine
Databases
Decision Support Systems
Factual
Female
Humans
Internet
Jia Zhenyu
Mercola Dan
Models
Prognosis
Proportional Hazards Models
Software
Theoretical
Xie Xiaohui
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/jep.13042</a>
Pages
1293–1309
Issue
6
Volume
24
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Exploring comorbid depression and physical health trajectories: A case-based computational modelling approach.
Publisher
An entity responsible for making the resource available
Journal of evaluation in clinical practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-12
Subject
The topic of the resource
artificial intelligence; case-based modelling; Child Abuse; cluster analysis; comorbid depression and physical health; Comorbidity; complexity theory; Computer Simulation; Depression – Therapy; differential equations; Health Status; Human; Intimate Partner Violence; longitudinal analysis; Models; nonlinear dynamics; primary care; Primary Health Care; Prospective Studies; Questionnaires; Research Personnel; Scales; Theoretical
Creator
An entity primarily responsible for making the resource
Castellani Brian; Griffiths Frances; Rajaram Rajeev; Gunn Jane
Description
An account of the resource
While comorbid depression/physical health is a major clinical concern, the conventional methods of medicine make it difficult to model the complexities of this relationship. Such challenges include cataloguing multiple trends, developing multiple complex aetiological explanations, and modelling the collective large-scale dynamics of these trends. Using a case-based complexity approach, this study engaged in a richly described case study to demonstrate the utility of computational modelling for primary care research. N = 259 people were subsampled from the Diamond database, one of the largest primary care depression cohort studies worldwide. A global measure of depressive symptoms (PHQ-9) and physical health (PCS-12) were assessed at 3, 6, 9, and 12 months and then annually for a total of 7 years. Eleven trajectories and 2 large-scale collective dynamics were identified, revealing that while depression is comorbid with poor physical health, chronic illness is often low dynamic and not always linked to depression. Also, some of the cases in the unhealthy and oscillator trends remain ill without much chance of improvement. Finally, childhood abuse, partner violence, and negative life events are greater amongst unhealthy trends. Computational modelling offers a major advance for health researchers to account for the diversity of primary care patients and for developing better prognostic models for team-based interdisciplinary care.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">10.1111/jep.13042</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
2018
Artificial Intelligence
case-based modelling
Castellani Brian
Child Abuse
Cluster Analysis
comorbid depression and physical health
Comorbidity
complexity theory
Computer Simulation
Depression – Therapy
differential equations
Griffiths Frances
Gunn Jane
Health Status
Human
Intimate Partner Violence
Journal of evaluation in clinical practice
longitudinal analysis
Models
Nonlinear Dynamics
primary care
Primary Health Care
Prospective Studies
Questionnaires
Rajaram Rajeev
Research Personnel
Scales
Theoretical