Histology image analysis of 13 healthy tissues reveals molecular-histological correlations by Yi Gao, Jianwen Liang, Mu Tian, Wenjiang Deng, Lei Wang, Siyuan Tao, Tian Mou

Subjects: “…Non–pathological tissue…”
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SP49. Machine Learning-derived Calculator to Predict Reoperations in Limb-sparing Upper Extremity Soft Tissue Sarcoma Reconstructions by Rami Elmorsi, MD, Luis Camacho, MD, David D. Krijgh, MD, Gordon S. Tilney, PA-C, Heather Lyu, MD, MBI, Raymond S. Traweek, MD, Russel G. Witt, MD, MAS, MS, Margaret S. Roubaud, MD, Christina L. Roland, MD, Alexander F. Mericli, MD

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Lymph Node Involvement Prediction Using Machine Learning: Analysis of Prostatic Nodule, Prostatic Gland, and Periprostatic Adipose Tissue (PPAT) by Eliodoro Faiella, Giulia D’amone, Raffaele Ragone, Matteo Pileri, Elva Vergantino, Bruno Beomonte Zobel, Rosario Francesco Grasso, Domiziana Santucci

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Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) by Daniel Lafontaine, Stephanie Chahwan, Gustavo Barraza, Burcin Agridag Ucpinar, Gunjan Kayal, Nicolás Gómez-Banoy, Paul Cohen, John L. Humm, Heiko Schöder

Subjects: “…Machine learning…”
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Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods by Ankita Lawarde, Ankita Lawarde, Masuma Khatun, Prakash Lingasamy, Prakash Lingasamy, Andres Salumets, Andres Salumets, Andres Salumets, Vijayachitra Modhukur, Vijayachitra Modhukur

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Developing supervised machine learning algorithms to classify lettuce foliar tissue samples into interpretation zones for 11 plant essential nutrients by Patrick Veazie, Hsuan Chen, Kristin Hicks, Jake Holley, Nathan Eylands, Neil Mattson, Jennifer Boldt, Devin Brewer, Roberto Lopez, Brian Whipker

“…This study examines four different machine learning algorithms (J48, random forest [RF], sequential minimal optimization [SMO], and multilayer perceptron [MLP]) by two different cross‐validation strategies (10‐fold and 66% split) to determine if machine learning can be utilized to accurately classify foliar tissue samples within corresponding nutrient ranges. …”
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Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms.... by David Dimitris Chlorogiannis, Georgios-Ioannis Verras, Vasiliki Tzelepi, Anargyros Chlorogiannis, Anastasios Apostolos, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos, Andreas Antzoulas, Michail Vailas, Dimitrios Schizas, Francesk Mulita

“…Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. …”
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Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer by Joshua Millward, Zhen He, Aiden Nibali, Dmitri Mouradov, Lisa A Mielke, Kelly Tran, Angela Chou, Nicholas J Hawkins, Robyn L Ward, Anthony J Gill, Oliver M Sieber, David S Williams

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Recent advances in pericardium extracellular matrix for tissue regeneration, along with a short insight into artificial intelligence by Parand Shariat Rad, Parand Shariat Rad, Mozafar Khazaei, Mozafar Khazaei, Elham Ghanbari, Mehdi Rashidi, Leila Rezakhani, Leila Rezakhani

“…Outside of the biological aspects, artificial intelligence (AI) and machine learning (ML) are applied to tissue engineering. Decellularization is a very important area where AI supports protocols and ensures the process is repeated identically each time. …”
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Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective by Parisa Shahnazari, Kaveh Kavousi, Zarrin Minuchehr, Bahram Goliaei, Reza M Salek

“…Abstract In this study, a comprehensive methodology combining machine learning and statistical analysis was employed to investigate alterations in the metabolite profiles, including lipids, of breast cancer tissues and their subtypes. …”
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Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies by Felix O. Hofmann, Christian Heiliger, Tengis Tschaidse, Stefanie Jarmusch, Liv A. Auhage, Ughur Aghamaliyev, Alena B. Gesenhues, Tobias S. Schiergens, Hanno Niess, Matthias Ilmer, Jens Werner, Bernhard W. Renz

“…In 337 surgical oncology patients, total skeletal muscle tissue (SMtotal), psoas muscle tissue (SMpsoas), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were quantified both manually and using the pipeline. …”
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Automated Detection of Connective Tissue by Tissue Counter Analysis and Classification and Regression Trees by Josef Smolle, Peter Kahofer

“…In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. …”
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A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids by Qinran Zhang, Huizhong Chi, Yanhua Qi, Rongrong Zhao, Fuzhong Xue, Gang Li, Hao Xue

“…This study developed the glioma-related cell signature (GRCS), a prediction model that integrates machine learning with biological insights. Trained on tumor-educated platelet samples, the GRCS model demonstrated consistent performance across validation cohorts comprising platelet, extracellular vesicle, and tumor tissue specimens. …”
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A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia by Jake Cumber, Jake Cumber, Emma Scales-Theobald, Emma Scales-Theobald, Clare Rusbridge, Clare Rusbridge, Clare Rusbridge, Kevin Wells, Kevin Wells

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Identification of Tissue Types and Gene Mutations From Histopathology Images for Advancing Colorectal Cancer Biology by Yuqi Jiang, Cecilia K. W. Chan, Ronald C. K. Chan, Xin Wang, Nathalie Wong, Ka Fai To, Simon S. M. Ng, James Y. W. Lau, Carmen C. Y. Poon

“…We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features. …”
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Editorial: Next-generation dental materials engineered for mineralized tissue reconstruction: advances, challenges and opportunities by Paulette Spencer, Paulette Spencer, Paulette Spencer, Conrado Aparicio, Conrado Aparicio, Conrado Aparicio, Conrado Aparicio, Candan Tamerler, Candan Tamerler, Candan Tamerler

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SlideTiler: A dataset creator software for boosting deep learning on histological whole slide images by Leonardo Barcellona, Lorenzo Nicolè, Rocco Cappellesso, Angelo Paolo Dei Tos, Stefano Ghidoni

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DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform by Shuli Sun, Jixin Liu, Guojun Li, Bingqiang Liu

“…However, high dropout rates and noise hinder accurate spatial domain identification for understanding tissue architecture. We present DeepGFT, a method that simultaneously models spot-wise and gene-wise relationships by integrating deep learning with graph Fourier transform for spatial domain identification. …”
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A Modified Parallel NET (MPNET)-Based Deep Learning Technique for the Segmentation and Quantification of Visceral and Superficial Adipose Tissues of CT Scans by Josteve Adekanbi, Debashish Das, Nouh Elmitwally, Aliyuda Ali, Vince I. Madai, Bahadar Bhatia, John Morlese, Muhammed Afsal, Tanay Shah

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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

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