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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Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles by Moneerah Alotaibi, Manal Abdullah Alohali, Khalid Mahmood, Asma A. Alhashmi, Jehad Saad Alqurni, Sultan Refa Alotaibi, Ahmad A. Alzahrani, Imene Issaoui

“…Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. …”
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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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AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE by X. Wang, L. Bi, H. Wang, Y. Wang, W. Han, X. Shen, X. Zhang

“…It incorporates the nonsphericity and inhomogeneity (NSIH) of internally mixed aerosol particles through a deep learning method. Specifically, the AI-NAOS considers black carbon (BC) to be fractal aggregates and models soil dust (SD) as super-spheroids, encapsulated partially or completely with hygroscopic aerosols such as sulfate, nitrate, and aerosol water. …”
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Artificial intelligence in orthopaedic trauma by Chuwei Tian, Yucheng Gao, Chen Rui, Shengbo Qin, Liu Shi, Yunfeng Rui

“…This study delves into the research progress and challenges of AI in orthopedic trauma, including the clinical applications of machine learning, deep learning, and natural language processing. By illuminating these dynamic research avenues, this study aimed to catalyze interdisciplinary collaboration and spur innovation at the intersection of AI and orthopedic trauma, ultimately advancing the frontiers of patient care and clinical practice.…”
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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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Research Progress and Prospect of Multi-robot Collaborative SLAM in Complex Agricultural Scenarios by MA Nan, CAO Shanshan, BAI Tao, KONG Fantao, SUN Wei

“…Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. …”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…In this study, based on the actual scenario of the Xiqing Tea Garden in Hunan Province, proposes a novel deep learning algorithm was proposed to solve the precise segmentation challenge of famous and high-quality tea picking points.…”
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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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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…Additionally, the review raises unresolved questions regarding the application of picking robots and outlines future trends, include deeper integration of stereo vision and deep learning, enhanced global vision sampling, and the establishment of standardized evaluation criteria for overall operational performance. …”
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Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm by Jingting Wei, Jingting Wei, Kunhong Xiao, Qingyuan Cai, Qingyuan Cai, Shenghua Lin, Xiangjie Lin, Yujie Wang, Yujie Wang, Jiawen Lin, Kunfeng Lai, Yunxi Ye, Yuhan Liu, Li Li

“…MG images were analyzed using a deep learning-based a quantitative analysis algorithm to evaluate gland length, area, dropout ratio, and deformation. …”
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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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Automated Quantification of Retinopathy of Prematurity Stage via Ultrawidefield OCT by Spencer S. Burt, BA, Aaron S. Coyner, PhD, Elizabeth V. Roti, BS, Yakub Bayhaqi, PhD, John Jackson, MD, Mani K. Woodward, MS, Shuibin Ni, PhD, Susan R. Ostmo, MS, Guangru Liang, BS, Yali Jia, PhD, David Huang, MD, Michael F. Chiang, MD, Benjamin K. Young, MD, Yifan Jian, PhD, John Peter Campbell, MD

“…This study evaluates whether the volume of anomalous NVT (ANVTV), defined as abnormal tissue protruding from the regular contour of the retina, can be measured automatically using deep learning to develop quantitative OCT-based biomarkers in ROP. …”
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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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