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Article ; Online: Artificial Intelligence for Clinical Flow Cytometry.

Seifert, Robert P / Gorlin, David A / Borkowski, Andrew A

2023 Volume 43, Issue 3, Page(s) 485–505

Abstract: In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their ... ...

Abstract	In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.
MeSH term(s)	Artificial Intelligence ; Flow Cytometry ; Machine Learning
Language	English
Publishing date	2023-05-29
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	604580-7
ISSN	1557-9832 ; 0272-2712
ISSN (online)	1557-9832
ISSN	0272-2712
DOI	10.1016/j.cll.2023.04.009
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Article ; Online: Pilot Lightweight Denoising Algorithm for Multiple Sclerosis on Spine MRI.

Mayfield, John D / Bailey, Katie / Borkowski, Andrew A / Viswanadhan, Narayan

Journal of digital imaging

2023 Volume 36, Issue 4, Page(s) 1877–1884

Abstract: Multiple sclerosis (MS) is a severely debilitating disease which requires accurate and timely diagnosis. MRI is the primary diagnostic vehicle; however, it is susceptible to noise and artifact which can limit diagnostic accuracy. A myriad of denoising ... ...

Abstract	Multiple sclerosis (MS) is a severely debilitating disease which requires accurate and timely diagnosis. MRI is the primary diagnostic vehicle; however, it is susceptible to noise and artifact which can limit diagnostic accuracy. A myriad of denoising algorithms have been developed over the years for medical imaging yet the models continue to become more complex. We developed a lightweight algorithm which utilizes the image's inherent noise via dictionary learning to improve image quality without high computational complexity or pretraining through a process known as orthogonal matching pursuit (OMP). Our algorithm is compared to existing traditional denoising algorithms to evaluate performance on real noise that would commonly be encountered in a clinical setting. Fifty patients with a history of MS who received 1.5 T MRI of the spine between the years of 2018 and 2022 were retrospectively identified in accordance with local IRB policies. Native resolution 5 mm sagittal images were selected from T2 weighted sequences for evaluation using various denoising techniques including our proposed OMP denoising algorithm. Peak signal to noise ratio (PSNR) and structural similarity index (SSIM) were measured. While wavelet denoising demonstrated an expected higher PSNR than other models, its SSIM was variable and consistently underperformed its comparators (0.94 ± 0.10). Our pilot OMP denoising algorithm provided superior performance with greater consistency in terms of SSIM (0.99 ± 0.01) with similar PSNR to non-local means filtering (NLM), both of which were superior to other comparators (OMP 37.6 ± 2.2, NLM 38.0 ± 1.8). The superior performance of our OMP denoising algorithm in comparison to traditional models is promising for clinical utility. Given its individualized and lightweight approach, implementation into PACS may be more easily incorporated. It is our hope that this technology will provide improved diagnostic accuracy and workflow optimization for Neurologists and Radiologists, as well as improved patient outcomes.
MeSH term(s)	Humans ; Multiple Sclerosis/diagnostic imaging ; Retrospective Studies ; Algorithms ; Tomography, X-Ray Computed/methods ; Magnetic Resonance Imaging/methods ; Signal-To-Noise Ratio ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-04-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	1033897-4
ISSN	1618-727X ; 0897-1889
ISSN (online)	1618-727X
ISSN	0897-1889
DOI	10.1007/s10278-023-00816-x
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Zs.A 2941: Show issues

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Article: TEMGYM Basic

Landers, David / Clancy, Ian / Weber, Dieter / Dunin-Borkowski, Rafal E / Stewart, Andrew

Journal of applied crystallography

2023 Volume 56, Issue Pt 4, Page(s) 1267–1276

Abstract: An interactive simulation of a transmission electron microscope (TEM) ... ...

Abstract	An interactive simulation of a transmission electron microscope (TEM) called
Language	English
Publishing date	2023-07-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2020879-0
ISSN	1600-5767 ; 0021-8898
ISSN (online)	1600-5767
ISSN	0021-8898
DOI	10.1107/S1600576723005174
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Article ; Online: Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Waqas, Asim / Bui, Marilyn M / Glassy, Eric F / El Naqa, Issam / Borkowski, Piotr / Borkowski, Andrew A / Rasool, Ghulam

Laboratory investigation; a journal of technical methods and pathology

2023 Volume 103, Issue 11, Page(s) 100255

Abstract: Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling ... ...

Abstract	Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.
MeSH term(s)	Humans ; Artificial Intelligence ; Image Processing, Computer-Assisted ; Machine Learning ; Neural Networks, Computer ; Pathologists ; Pathology/trends
Language	English
Publishing date	2023-09-26
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	80178-1
ISSN	1530-0307 ; 0023-6837
ISSN (online)	1530-0307
ISSN	0023-6837
DOI	10.1016/j.labinv.2023.100255
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Article ; Online: TEMGYM Advanced - NanoMi lens characterisation.

Landers, David / Clancy, Ian / Dunin-Borkowski, Rafal E / Weber, Dieter / Stewart, Andrew A

Micron (Oxford, England : 1993)

2023 Volume 169, Page(s) 103450

Abstract: A complete analysis including finite element method (FEM) calculation, focal length properties, and thirdorder geometric aberrations of the open-source electrostatic lens from the NanoMi project is presented. The analysis is carried out by the software ... ...

Abstract	A complete analysis including finite element method (FEM) calculation, focal length properties, and thirdorder geometric aberrations of the open-source electrostatic lens from the NanoMi project is presented. The analysis is carried out by the software TEMGYM Advanced, a free package developed to carry out ray-tracing and lens characterisation in Python. Previously TEMGYM Advanced has shown how to analyse the aberrations of analytical lens fields; this paper expands upon this work to demonstrate how to apply a suitable fitting method to discrete lens fields obtained via FEM methods so that the aberrations of real lens designs can be calculated. Each software platform used in this paper is freely available in the community and creates a free and viable alternative to commercial lens design packages.
Language	English
Publishing date	2023-03-27
Publishing country	England
Document type	Journal Article
ZDB-ID	207808-9
ISSN	1878-4291 ; 0047-7206 ; 0968-4328
ISSN (online)	1878-4291
ISSN	0047-7206 ; 0968-4328
DOI	10.1016/j.micron.2023.103450
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Article ; Online: TEMGYM Advanced: Software for electron lens aberrations and parallelised electron ray tracing.

Landers, David / Clancy, Ian / Dunin-Borkowski, Rafal E / Weber, Dieter / Stewart, Andrew

Ultramicroscopy

2023 Volume 250, Page(s) 113738

Abstract: Characterisation of the electron beams trajectory in an electron microscope is possible in a few select commercial software packages, but these tools and their source code are not available in a free and accessible manner. This paper introduces the free ... ...

Abstract	Characterisation of the electron beams trajectory in an electron microscope is possible in a few select commercial software packages, but these tools and their source code are not available in a free and accessible manner. This paper introduces the free and open-source software TEMGYM Advanced, which implements ray tracing methods that calculate the path of electrons through a magnetic or electrostatic lens and allow evaluation of the first-order properties and third-order geometric aberrations. Validation of the aberration coefficient calculations is performed by implementing two independent methods - the aberration integral and differential algebra (DA) methods and by comparing the results of each. This paper also demonstrates parallelised electron ray tracing through a series of magnetic components, which enables near real-time generation of a physically accurate beam-spot including aberrations and brings closer the realisation of a digital twin of an electron microscope. TEMGYM Advanced represents a valuable resource for the electron microscopy community, providing an accessible and open source means of characterising electron lenses. This software utilises the Python programming language to complement the growing ecosystem of free and open-source software within the electron microscopy community, and to facilitate the application of machine learning to an electron microscope digital twin for instrument automation. The software is available under GNU Public License number Three (GPL 3).
Language	English
Publishing date	2023-04-05
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479043-9
ISSN	1879-2723 ; 0304-3991
ISSN (online)	1879-2723
ISSN	0304-3991
DOI	10.1016/j.ultramic.2023.113738
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Article: Artificial Intelligence: Review of Current and Future Applications in Medicine.

Thomas, L Brannon / Mastorides, Stephen M / Viswanadhan, Narayan A / Jakey, Colleen E / Borkowski, Andrew A

Federal practitioner : for the health care professionals of the VA, DoD, and PHS

2022 Volume 38, Issue 11, Page(s) 527–538

Abstract: Background: The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in ... ...

Abstract	Background: The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. Observations: With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. Conclusions: AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.
Language	English
Publishing date	2022-02-04
Publishing country	United States
Document type	Journal Article ; Review
ISSN	1078-4497
ISSN	1078-4497
DOI	10.12788/fp.0174
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Article ; Online: Show Me The Money…Saved! Cost Savings From Acute Asthma Care in the School-Based Health Center.

Goddard, Anna / Konesky, Andrew / Borkowski, Vera / Etcher, LuAnn

The Journal of school nursing : the official publication of the National Association of School Nurses

2021 Volume 38, Issue 2, Page(s) 210–219

Abstract: Chronic school absenteeism directly affects educational outcomes while reducing school funding and reimbursement. Asthma is a prevalent disease associated with chronic absenteeism. This quality improvement project demonstrated classroom seat time ... ...

Abstract	Chronic school absenteeism directly affects educational outcomes while reducing school funding and reimbursement. Asthma is a prevalent disease associated with chronic absenteeism. This quality improvement project demonstrated classroom seat time preserved through use of school-based health centers (SBHC). The project also highlights the educational benefits, reduced emergency department utilization, potential cost savings to hospitals, and lower overall health care costs. Visit summary data were collected and analyzed to show quality asthma care and cost savings. Of 44 acute asthma visits that returned to class, an average classroom time of 3:42 hours were saved per student during the 2017-2018 academic year, resulting in a combined total of 166:07 hours saved. A minimum potential cost savings was estimated to be $67,770 for all 44 visits. Data analysis of structural, process, and outcome measures through quality improvement tools can demonstrate cost savings of SBHC care, which advocates funding for this pediatric care model.
MeSH term(s)	Absenteeism ; Asthma/therapy ; Child ; Cost Savings ; Humans ; School Nursing ; Schools
Language	English
Publishing date	2021-01-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	1217746-5
ISSN	1546-8364 ; 1059-8405 ; 0048-945X
ISSN (online)	1546-8364
ISSN	1059-8405 ; 0048-945X
DOI	10.1177/1059840520986951
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Article: Neuroimaging in the Era of Artificial Intelligence: Current Applications.

Monsour, Robert / Dutta, Mudit / Mohamed, Ahmed-Zayn / Borkowski, Andrew / Viswanadhan, Narayan A

Federal practitioner : for the health care professionals of the VA, DoD, and PHS

2022 Volume 39, Issue Suppl 1, Page(s) S14–S20

Abstract: Background: Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and ...

Abstract	Background: Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care. Observations: AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology. Conclusions: The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.
Language	English
Publishing date	2022-04-12
Publishing country	United States
Document type	Journal Article ; Review
ISSN	1078-4497
ISSN	1078-4497
DOI	10.12788/fp.0231
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Article: Establishing a Hospital Artificial Intelligence Committee to Improve Patient Care.

Borkowski, Andrew A / Jakey, Colleen E / Thomas, L Brannon / Viswanadhan, Narayan / Mastorides, Stephen M

Federal practitioner : for the health care professionals of the VA, DoD, and PHS

2022 Volume 39, Issue 8, Page(s) 334–336

Abstract: Background: The use of artificial intelligence (AI) in health care is increasing and has shown utility in many medical specialties, especially pathology, radiology, and oncology.: Observations: Many barriers exist to successfully implement AI ... ...

Abstract	Background: The use of artificial intelligence (AI) in health care is increasing and has shown utility in many medical specialties, especially pathology, radiology, and oncology. Observations: Many barriers exist to successfully implement AI programs in the clinical setting. To address these barriers, a formal governing body, the hospital AI Committee, was created at James A. Haley Veterans' Hospital in Tampa, Florida. The AI committee reviews and assesses AI products based on their success at protecting human autonomy; promoting human well-being and safety and the public interest; ensuring transparency, explainability, and intelligibility; fostering responsibility and accountability; ensuring inclusiveness and equity; and promoting AI that is responsive and sustainable. Conclusions: Through the hospital AI Committee, we may overcome many obstacles to successfully implementing AI applications in the clinical setting.
Language	English
Publishing date	2022-08-10
Publishing country	United States
Document type	Journal Article
ISSN	1078-4497
ISSN	1078-4497
DOI	10.12788/fp.0299
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