Роль искусственного интеллекта в прогнозировании трудных дыхательных путей у взрослых: обзор литературы... by Андрей Юрьевич Зайцев, А. Б. Сорокин, Ю. А. Зайцев, К. В. Дубровин, Э. Г. Усикян

“…Поисковыми словами для англоязычных баз данных были artificial intelligence, deep learning, difficult airways; для русскоязычных — искусственный интеллект, глубокое машинное обучение, трудные дыхательные пути. …”
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Use of artificial intelligence for gestational age estimation: a systematic review and meta-analysis by Sabahat Naz, Sahir Noorani, Syed Ali Jaffar Zaidi, Abdu R. Rahman, Saima Sattar, Jai K. Das, Jai K. Das, Zahra Hoodbhoy

“…On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). …”
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ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary by Liying Zhu, Yabin Hu, Guangbo Ren, Na Qiao, Ziyue Meng, Jianbu Wang, Yajie Zhao, Shibao Li, Yi Ma

“…To address these challenges, this study proposes a deep learning-based cross-domain classification model, ER-GMMD, which leverages features extracted by deep residual networks for different mixed-growth patterns of <italic>tamarix chinensis,</italic> and integrates dual feature alignment to address the cross-scene classification challenges of mixed-species <italic>tamarix chinensis</italic>. …”
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Signatures of H3K4me3 modification predict cancer immunotherapy response and identify a new immune checkpoint-SLAMF9 by Tao Fan, Chu Xiao, Ziqin Deng, Shuofeng Li, He Tian, Yujia Zheng, Bo Zheng, Chunxiang Li, Jie He

“…Using the principal component analysis (PCA) of H3K4me3-related patterns, we constructed a H3K4me3 risk score (H3K4me3-RS) system. The deep learning analysis using 12,159 cancer samples from 26 cancer types and 725 cancer samples from 5 immunotherapy cohorts revealed that H3K4me3-RS was significantly correlated with cancer immune tolerance and sensitivity. …”
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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Methods based on machine learning, and deep learning, rely on a large number of training samples, which is time-consuming and laborious. …”
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PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology by Kevin Faust, Min Li Chen, Parsa Babaei Zadeh, Dimitrios G. Oreopoulos, Alberto J. Leon, Ameesha Paliwal, Evelyn Rose Kamski-Hennekam, Marly Mikhail, Xianpi Duan, Xianzhao Duan, Mugeng Liu, Narges Ahangari, Raul Cotau, Vincent Francis Castillo, Nikfar Nikzad, Richard J. Sugden, Patrick Murphy, Safiyh S. Aljohani, Philippe Echelard, Susan J. Done, Kiran Jakate, Zaid Saeed Kamil, Yazeed Alwelaie, Mohammed J. Alyousef, Noor Said Alsafwani, Assem Saleh Alrumeh, Rola M. Saleeb, Maxime Richer, Lidiane Vieira Marins, George M. Yousef, Phedias Diamandis

“…Abstract Deep learning has proven capable of automating key aspects of histopathologic analysis. …”
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Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery by Arda Halu, Sarvesh Chelvanambi, Julius L. Decano, Joan T. Matamalas, Mary Whelan, Takaharu Asano, Namitra Kalicharran, Sasha A. Singh, Joseph Loscalzo, Masanori Aikawa

“…Pathopticon demonstrates a better prediction performance than solely cheminformatic measures as well as state-of-the-art network and deep learning-based methods. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. …”
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Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing by Arief Setyanto, Theopilus Bayu Sasongko, Muhammad Ainul Fikri, Dhani Ariatmanto, I. Made Artha Agastya, Rakandhiya Daanii Rachmanto, Affan Ardana, In Kee Kim

“…Although state-of-the-art deep learning-based OD methods achieve high detection rates, their large model size and high computational demands often hinder deployment on resource-constrained edge devices. …”
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Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model by Maria Colomba Comes, Annarita Fanizzi, Samantha Bove, Luca Boldrini, Agnese Latorre, Deniz Can Guven, Serena Iacovelli, Tiziana Talienti, Alessandro Rizzo, Francesco Alfredo Zito, Raffaella Massafra

“…Aims This study aimed to develop an ensemble deep learning‐based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre‐ and mid‐treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC. …”
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The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance by Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan, Muhammad Shafiq, Wenjie Fang, Rahat Ullah Khan, Mujeeb Ur Rahman, Xiaohui Li, Qiao-Li Lv, Bin Xu

“…Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. …”
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ChromaFold predicts the 3D contact map from single-cell chromatin accessibility by Vianne R. Gao, Rui Yang, Arnav Das, Renhe Luo, Hanzhi Luo, Dylan R. McNally, Ioannis Karagiannidis, Martin A. Rivas, Zhong-Min Wang, Darko Barisic, Alireza Karbalayghareh, Wilfred Wong, Yingqian A. Zhan, Christopher R. Chin, William S. Noble, Jeff A. Bilmes, Effie Apostolou, Michael G. Kharas, Wendy Béguelin, Aaron D. Viny, Danwei Huangfu, Alexander Y. Rudensky, Ari M. Melnick, Christina S. Leslie

“…We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone. …”
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Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics by Stefan Schoder

“…The hyperparameter study and optimization are conducted regarding the network depth and width, the learning rate, the used activation functions, and the deep learning backends (PyTorch 2.5.1, TensorFlow 2.18.0 1, TensorFlow 2.18.0 2, JAX 0.4.39). …”
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A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging by Zheyi Dong, Xiaofei Wang, Sai Pan, Taohan Weng, Xiaoniao Chen, Shuangshuang Jiang, Ying Li, Zonghua Wang, Xueying Cao, Qian Wang, Pu Chen, Lai Jiang, Guangyan Cai, Li Zhang, Yong Wang, Jinkui Yang, Yani He, Hongli Lin, Jie Wu, Li Tang, Jianhui Zhou, Shengxi Li, Zhaohui Li, Yibing Fu, Xinyue Yu, Yanqiu Geng, Yingjie Zhang, Liqiang Wang, Mai Xu, Xiangmei Chen

“…To reform the traditional biopsy-all diagnostic paradigm and avoid unnecessary biopsy, we developed a transformer-based deep learning (DL) system for detecting DN and NDRD upon non-invasive multi-modal data of fundus images and clinical characteristics. …”
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An Arctic sea ice concentration data record on a 6.25&thinsp;km polar stereographic grid from 3 years of Landsat-8 imagery by H.-S. Jung, S.-M. Lee, S.-M. Lee, J.-H. Kim, K. Lee

“…The vast amount of Landsat-8 SIC generated in this study may also be used to train deep-learning models for the estimation of Arctic SIC coverage. …”
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A comprehensive environmental index for monitoring ecological quality of typical alpine wetlands in Central Asia by Jiudan Zhang, Junli Li, Changming Zhu, Anming Bao, Amaury Frankl, Philippe De Maeyer, Tim Van de Voorde

“…This study employed a deep-learning semantic segmentation model to map the structural changes of the Bayanbulak alpine wetland using Landsat imagery from 1977 to 2022. …”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…<p>Deep learning models have demonstrated remarkable success in fields such as language processing and computer vision, routinely employed for tasks like language translation, image classification, and anomaly detection. …”
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scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics by Vishakha Gautam, Subhadeep Duari, Saveena Solanki, Mudit Gupta, Aayushi Mittal, Sakshi Arora, Anmol Aggarwal, Anmol Kumar Sharma, Sarthak Tyagi, Rathod Kunal Pankajbhai, Arushi Sharma, Sonam Chauhan, Shiva Satija, Suvendu Kumar, Sanjay Kumar Mohanty, Juhi Tayal, Nilesh Kumar Dixit, Debarka Sengupta, Anurag Mehta, Gaurav Ahuja

“…Summary: Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. …”
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Utilisation of ChatGPT and other Artificial Intelligence tools among medical faculty in Uganda: a cross-sectional study [version 2; peer review: 1 approved, 2 approved with reserva... by Job Kuteesa, Faith Oguttu, Ian Guyton Munabi, Aloysius Gonzaga Mubuuke, Sarah Kiguli, Ritah Nantale, David Mukunya, Elizabeth Ajalo, Frank Kayemba, Jonathan Babuya, Kennedy Pangholi, Amelia Margaret Namiiro, Suzan Langoya Akuu, Steven Ekak, Ronald Tweheyo, Kirsten Nantongo, Brenda Nakitto, Milton W. Musaba, Joseph Luwaga Mpagi

“…Background ChatGPT is a large language model that uses deep learning techniques to generate human-like texts. …”
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Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types by Jeanne Shen, Sergio Pereira, Chan-Young Ock, Yung-Jue Bang, Seulki Kim, Sehhoon Park, Se-Hoon Lee, George A Fisher, Young Kwang Chae, Yoon-La Choi, Jin-Haeng Chung, Tony S K Mok, Leeseul Kim, Jun-Eul Hwang, Gahee Park, Sanghoon Song, Seunghwan Shin, Yoojoo Lim, Wonkyung Jung, Heon Song, Hyojin Kim, Taebum Lee, Sukjun Kim, Chang Ho Ahn, Seokhwi Kim, Ben W Dulken, Stephanie Bogdan, Maggie Huang, Chiyoon Oum, Siraj M. Ali

“…Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types.Methods Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. …”
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Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging by Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller-Franzes, Zunamys I. Carrero, Nicholas R. Payne, Lorena Escudero Sánchez, Paul Christophe Varoutas, Sreenath Kyathanahally, Narmin Ghaffari Laleh, Kevin Pfeiffer, Marta Ligero, Jakob Behner, Kamarul A. Abdullah, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos, Cristina Rossi, Sylwia Nowakowska, Alexandra Athanasiou, Raquel Perez-Lopez, Ritse Mann, Wouter Veldhuis, Julia Camps, Volkmar Schulz, Markus Wenzel, Sergey Morozov, Alexander Ciritsis, Christiane Kuhl, Fiona J. Gilbert, Daniel Truhn, Jakob Nikolas Kather

“…Results Upon systematically benchmarking various weakly supervised two-dimensional (2D) and three-dimensional (3D) deep learning (DL) methods, we find that the 3D-ResNet-101 demonstrates superior performance. …”
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