Article
Multidisciplinary Sciences
Lei Jin, Tianyang Sun, Xi Liu, Zehong Cao, Yan Liu, Hong Chen, Yixin Ma, Jun Zhang, Yaping Zou, Yingchao Liu, Feng Shi, Dinggang Shen, Jinsong Wu
Summary: Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. In this study, deep learning techniques were used for automated histological pathology diagnosis of gliomas. The model showed high accuracy in both internal validation and multi-center testing.
Article
Pathology
Diana Montezuma, Sara P. Oliveira, Pedro C. Neto, Domingos Oliveira, Ana Monteiro, Jaime S. Cardoso, Isabel Macedo-Pinto
Summary: In this paper, the authors describe their experience in training machine learning models for AI applications in pathology, which often requires extensive annotation by human experts. They provide a simple and practical guide addressing annotation strategies for AI development in computational pathology, covering team interaction, ground-truth quality assessment, annotation types, and available software and hardware options. This guide aims to assist pathologists, researchers, and AI developers in the annotation process.
Article
Cell Biology
Celine N. Heinz, Amelie Echle, Sebastian Foersch, Andrey Bychkov, Jakob Nikolas Kather
Summary: This study surveyed 75 computational pathology domain experts and found that predicting treatment response directly from routine pathology slides is considered the most promising application in the future. Additionally, predicting genetic alterations, gene expression, and survival directly from routine pathology images also scored consistently high.
Review
Medicine, General & Internal
Mihaela Moscalu, Roxana Moscalu, Cristina Gena Dascalu, Viorel Tarca, Elena Cojocaru, Ioana Madalina Costin, Elena Tarca, Ionela Lacramioara Serban
Summary: In modern clinical practice, digital pathology plays an essential role in pathological anatomy laboratories. The development of information technology has greatly facilitated the management and sharing of digital images for clinical use. By using artificial intelligence techniques and specific models, the analysis of digital histopathological images can provide more consistent and precise information compared to optical microscopy.
Article
Gastroenterology & Hepatology
Jun Ohara, Tetsuo Nemoto, Yasuharu Maeda, Noriyuki Ogata, Shin-Ei Kudo, Toshiko Yamochi
Summary: This study demonstrates that an automated quantitative method using a deep learning-based model is useful in predicting the prognosis of patients with ulcerative colitis by evaluating mucin depletion.
JOURNAL OF GASTROENTEROLOGY
(2022)
Article
Engineering, Electrical & Electronic
Sandra Morales, Kjersti Engan, Valery Naranjo
Summary: The field of digital histopathology has seen significant growth, offering important tools for healthcare, industrial, and research sectors to improve diagnostic accuracy and reduce turnaround times in pathology. The future of computational pathology is expected to involve the integration of AI with strategies such as weak labeling, active learning, and crowdsourcing to address current challenges. Areas such as explainable AI, data fusion, and secure role-based data sharing are likely to receive increasing research attention in computational pathology.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Medicine, General & Internal
Kai Rakovic, Richard Colling, Lisa Browning, Monica Dolton, Margaret R. Horton, Andrew Protheroe, Alastair D. Lamb, Richard J. Bryant, Richard Scheffer, James Crofts, Ewart Stanislaus, Clare Verrill
Summary: The deployment of digital pathology and artificial intelligence in the diagnosis of prostate cancer has attracted particular interest. A survey conducted among Prostate Cancer UK supporters revealed that the majority of respondents were supportive of these technologies, seeing their potential in improving workflow efficiency and facilitating clinical discussions. However, concerns were raised regarding data security and reliability.
Article
Biochemistry & Molecular Biology
M. P. Humphries, P. Maxwell, M. Salto-Tellez
Summary: QuPath, created at Queen's University Belfast, is the most widely used image analysis software program globally, addressing various needs in tissue-based image analysis and serving as the system of choice for researchers in scientific research.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Medicine, Research & Experimental
Corey S. Post, Jerome Cheng, Liron Pantanowitz, Maria Westerhoff
Summary: Rapid and accurate CMV identification in immunosuppressed or immuno-compromised patients presenting with diarrhea is essential. This study proposes the use of artificial intelligence to detect CMV inclusions on routine H & E-stained whole-slide images to aid pathologists in evaluating these cases.
LABORATORY INVESTIGATION
(2023)
Review
Health Care Sciences & Services
Chiara Frascarelli, Giuseppina Bonizzi, Camilla Rosella Musico, Eltjona Mane, Cristina Cassi, Elena Guerini Rocco, Annarosa Farina, Aldo Scarpa, Rita Lawlor, Luca Reggiani Bonetti, Stefania Caramaschi, Albino Eccher, Stefano Marletta, Nicola Fusco
Summary: This paper explores how AI and machine learning can respond to the digital evolution of biobanks. By leveraging AI, biobanks can address the challenges faced in translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. The application of AI can unlock valuable insights from biobank repositories, enabling the identification of novel biomarkers and prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Review
Oncology
Nicolo Caldonazzi, Paola Chiara Rizzo, Albino Eccher, Ilaria Girolami, Giuseppe Nicolo Fanelli, Antonio Giuseppe Naccarato, Giuseppina Bonizzi, Nicola Fusco, Giulia d'Amati, Aldo Scarpa, Liron Pantanowitz, Stefano Marletta
Summary: The assessment of lymph node metastases is important in cancer staging and prognosis. By applying artificial intelligence to whole slide images, the automatic detection of metastatic cells can be achieved, leading to increased diagnostic quality. This study reviews the literature on using AI for the assessment of metastases in lymph nodes in whole slide images.
Article
Medicine, Research & Experimental
Ava Slotman, Minqi Xu, Katherine Lindale, Celine Hardy, Dan Winkowski, Regan Baird, Lina Chen, Priti Lal, Theodorus van der Kwast, Chelsea L. Jackson, Robert J. Gooding, David M. Berman
Summary: This study aimed to measure morphometric features relevant to grading criteria in noninvasive papillary urothelial carcinoma (NPUC) and build simplified classification models to distinguish between different grades objectively. The findings indicate that nuclear morphometry and automated mitotic figure counts can be used to objectively differentiate the grades of NPUC. These quantitative elements have the potential to revolutionize pathologic assessment and improve the prognostic utility of grade.
LABORATORY INVESTIGATION
(2023)
Review
Oncology
Amandine Crombe, Mathtieu Roulleau-Dugage, Antoine Italiano
Summary: Soft-tissue sarcomas (STS) are rare and heterogeneous tumors with challenges in diagnosis and limited treatment options. Digital pathology and radiomics show promise in improving accuracy of diagnosis, characterization, and monitoring. Immunotherapy may be an efficient therapeutic strategy for STS, with potential in revolutionizing treatment.
CANCER COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Chris Gorman, Davide Punzo, Igor Octaviano, Steven Pieper, William J. R. Longabaugh, David A. Clunie, Ron Kikinis, Andrey Y. Fedorov, Markus D. Herrmann
Summary: The article introduces Slim, an open-source, web-based slide microscopy viewer that implements the DICOM standard to achieve interoperability. It showcases Slim as the viewer for the NCI Imaging Data Commons and demonstrates its capabilities in interactive visualization of different types of microscopy images using standard DICOMweb services. The article also highlights Slim's ability to collect standardized image annotations for machine learning models and interpret model inference results.
NATURE COMMUNICATIONS
(2023)
Review
Oncology
Vidya Sankar Viswanathan, Paula Toro, German Corredor, Sanjay Mukhopadhyay, Anant Madabhushi
Summary: The development of digital pathology and artificial intelligence has provided new opportunities for the diagnosis and treatment of lung diseases. Research has explored the application of AI methods and tools in lung diseases, including image analysis and the use of biomarkers. AI tools also have the potential to play a role in areas such as multimodal data analysis, 3D pathology, and transplant rejection in lung diseases.
JOURNAL OF PATHOLOGY
(2022)
Article
Materials Science, Multidisciplinary
Meryem Uzun-Per, Gregory J. Gillispie, Thomas Erol Tavolara, James J. Yoo, Anthony Atala, Metin Nafi Gurcan, Sang Jin Lee, Muhammad Khalid Khan Niazi
Summary: The lack of suitable bioinks for bioprinting is a major limitation in tissue engineering and regenerative medicine, primarily due to the contradictory requirements of bioinks needing to exhibit desirable bioactivity while also being highly printable. This study proposes methods and tools for automatically quantifying the performance of bioinks, reducing the time and effort needed for analysis and providing a standardized set of tools for comparison.
ADVANCED ENGINEERING MATERIALS
(2021)
Article
Microbiology
Deniz Koyuncu, Muhammad Khalid Khan Niazi, Thomas Tavolara, Claudia Abeijon, Melanie L. Ginese, Yanghui Liao, Carolyn Mark, Aubrey Specht, Adam C. Gower, Blanca I. Restrepo, Daniel M. Gatti, Igor Kramnik, Metin Gurcan, Buelent Yener, Gillian Beamer
Summary: The study identified five protein biomarker candidates using a Diversity Outbred mouse population, with CXCL1 and MMP8 being the most promising. Through statistical and machine learning analysis, CXCL1 was found to meet the World Health Organization's criteria for a triage diagnostic test to distinguish active TB from other conditions in human patients.
Article
Health Policy & Services
James E. Peacock, David M. Herrington, Sharon L. Edelstein, Austin L. Seals, Ian D. Plumb, Sharon Saydah, William H. Lagarde, Michael S. Runyon, Patrick D. Maguire, Adolfo Correa, William S. Weintraub, Thomas F. Wierzba, John W. Sanders
Summary: Prevention behaviors are crucial to limiting the spread of SARS-CoV-2, yet a survey of over 20,000 individuals in the US found that most did not fully adhere to recommended public health safety measures during holiday gatherings following Thanksgiving and the winter holidays. Women were more likely to gather with non-household members (NHM), while older individuals and non-Hispanic Whites were more likely to wear masks when NHM were present. The extent to which failure to follow these recommendations contributed to the COVID-19 surges observed post-holidays remains uncertain.
JOURNAL OF COMMUNITY HEALTH
(2022)
Review
Pathology
Claudio Luchini, Liron Pantanowitz, Volkan Adsay, Sylvia L. Asa, Pietro Antonini, Ilaria Girolami, Nicola Veronese, Alessia Nottegar, Sara Cingarlini, Luca Landoni, Lodewijk A. Brosens, Anna V. Verschuur, Paola Mattiolo, Antonio Pea, Andrea Mafficini, Michele Milella, Muhammad K. Niazi, Metin N. Gurcan, Albino Eccher, Ian A. Cree, Aldo Scarpa
Summary: Ki-67 assessment plays a key role in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Digital pathology combined with machine learning has shown to be highly accurate and reproducible for evaluating Ki-67 in NENs. In this systematic review, the advantages of digital image analysis (DIA) in assessing Ki-67 in pancreatic NENs (PanNENs) were highlighted, including improved standardization and reliability, as well as increased speed and practicality compared to manual counting. However, limitations such as higher costs and operator qualification issues need to be addressed. A comparative meta-analysis showed a high concordance between DIA and manual counting. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs.
Article
Computer Science, Artificial Intelligence
Hamidullah Binol, M. Khalid Khan Niazi, Charles Elmaraghy, Aaron C. Moberly, Metin N. Gurcan
Summary: The lack of objective evaluation methods for the eardrum is a critical barrier to accurate diagnosis. This paper proposes a novel deep learning-based method called OtoXNet, which automatically learns features for eardrum classification from otoscope video clips. By utilizing multiple composite image generation methods, OtoXNet proves to outperform baseline approaches in qualitative results, showing the advantage of using multiple composite images in analyzing eardrum abnormalities.
NEURAL COMPUTING & APPLICATIONS
(2022)
Review
Otorhinolaryngology
Stephany Ngombu, Hamidullah Binol, Metin N. Gurcan, Aaron C. Moberly
Summary: This review discusses the state of the art applications of artificial intelligence (AI) techniques in diagnosing otitis media (OM) and highlights the potential benefits of using AI to automate and aid in diagnosis.
OTOLARYNGOLOGY-HEAD AND NECK SURGERY
(2023)
Article
Computer Science, Artificial Intelligence
Ziyu Su, Thomas E. Tavolara, Gabriel Carreno-Galeano, Sang Jin Lee, Metin N. Gurcan, M. K. K. Niazi
Summary: This study proposes attention2majority, a weak multiple instance learning model, to automatically and efficiently process whole slide images (WSIs) of stained tissue sections for classification. By using intelligent sampling and a multi-head attention-based multiple instance learning model, slide-level classification based on high-confidence patches is achieved.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Public, Environmental & Occupational Health
Lydia E. Calamari, Ashley H. Tjaden, Sharon L. Edelstein, William S. Weintraub, Roberto Santos, Michael Gibbs, Johnathan Ward, Michele Santacatterina, Alain G. Bertoni, Lori M. Ward, Sharon Saydah, Ian D. Plumb, Michael S. Runyon
Summary: This study investigated self-reported mask use among participants in the COVID-19 Community Research Partnership (CRP) and found that mask use was higher among vaccinated participants and those aged 65 years and older, female, racial or ethnic minority group, and healthcare workers. Lower mask use was associated with a history of self-reported prior COVID-19 illness.
PREVENTIVE MEDICINE REPORTS
(2022)
Article
Oncology
Thomas E. Tavolara, Metin N. Gurcan, M. Khalid Khan Niazi
Summary: This study proposes an unsupervised method to learn meaningful features from histopathological imaging data. The method achieves high accuracy and correlation in classifying non-small cell lung cancer subtypes and scoring breast cancer proliferation. The significance of this method lies in its ability to learn meaningful features from raw imaging data without slide-level annotations.
Proceedings Paper
Computer Science, Information Systems
Thomas E. Tavolara, M. Khalid Khan Niazi, Gary Tozbikian, Robert Wesolowski, Metin N. Gurcan
Summary: This study developed an automated method to predict HER2 scores in breast cancer, using immunohistochemical staining images and tissue sections. The preliminary results showed potential for localizing and scoring HER2 using H&E images.
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY
(2022)
Proceedings Paper
Computer Science, Information Systems
Thomas E. Tavolara, Arijit Dutta, Martin Burks, Wei Chen, Wendy Frankel, Metin N. Gurcan, M. Khalid Khan Niazi
Summary: This study successfully developed an automated algorithm that combines routine H&E staining with pan-cytokeratin staining to generate ground truth for tumor budding. The results demonstrated the potential feasibility of this method in identifying tumor buds.
MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY
(2022)
Review
Engineering, Biomedical
Diana Lim, Eric S. Renteria, Drake S. Sime, Young Min Ju, Ji Hyun Kim, Tracy Criswell, Thomas D. Shupe, Anthony Atala, Frank C. Marini, Metin N. Gurcan, Shay Soker, Joshua Hunsberger, James J. Yoo
Summary: Regenerative medicine and tissue engineering provide new therapeutic options for restoring, maintaining, or improving tissue function. To optimize the biological function of tissue-engineered clinical products, specific conditions must be maintained in a bioreactor to allow product maturation and mimic the in vivo environment. Real-time monitoring of product functional capacity is critical for quality management during manufacturing.
BIO-DESIGN AND MANUFACTURING
(2022)
Article
Health Policy & Services
Martin S. Kohn, Umit Topaloglu, Eric S. Kirkendall, Ajay Dharod, Brian J. Wells, Metin Gurcan
Summary: The nature of information in medicine has changed with the availability of massive, diverse data streams; a Learning Health System facilitates the development of medical decision-making tools and demonstrates enhanced value in decision-making; clinicians need to acquire skills necessary to work with big data in this era.
LEARNING HEALTH SYSTEMS
(2022)
Article
Oncology
Suraj Rajendran, Jihad S. Obeid, Hamidullah Binol, Ralph D'Agostino, Kristie Foley, Wei Zhang, Philip Austin, Joey Brakefield, Metin N. Gurcan, Umit Topaloglu
Summary: This study explores several federated learning implementations in healthcare using electronic health record data from two academic medical centers. Results show a 3% increase in performance for cyclically trained artificial neural networks, while logistic regression models did not show much improvement. The order of institutions during training influenced the overall performance increase. More work is needed to achieve effective federated learning processes in biomedicine.
JCO CLINICAL CANCER INFORMATICS
(2021)
Article
Medicine, Research & Experimental
Hamidullah Binol, Muhammad Khalid Khan Niazi, Garth Essig, Jay Shah, Jameson K. Mattingly, Michael S. Harris, Charles Elmaraghy, Theodoros Teknos, Nazhat Taj-Schaal, Lianbo Yu, Metin N. Gurcan, Aaron C. Moberly
Summary: This study investigated whether a single composite image stitched from a digital otoscopy video can provide sufficient diagnostic information for accurate diagnosis, and found that it can indeed provide accurate diagnostic information.
