Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study by Chengkun Sun, Erin Mobley, Michael Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad Awad, Jennifer Fishe, Alexander Parker, Thomas George, Jiang Bian, Jie Xu

“…By uncovering important risk factors and achieving promising predictive performance, this study provides preliminary insights that could inform future efforts toward earlier detection and prevention in younger populations.…”
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Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews by He-Li Xu, Ting-Ting Gong, Xin-Jian Song, Qian Chen, Qi Bao, Wei Yao, Meng-Meng Xie, Chen Li, Marcin Grzegorzek, Yu Shi, Hong-Zan Sun, Xiao-Han Li, Yu-Hong Zhao, Song Gao, Qi-Jun Wu

“…In the case of breast cancer detection, 8 reviews calculated the pooled sensitivity and specificity within the ranges of 75.4%-92% and 83%-90.6%, respectively. …”
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Development of a Mobile Intervention for Procrastination Augmented With a Semigenerative Chatbot for University Students: Pilot Randomized Controlled Trial by Seonmi Lee, Jaehyun Jeong, Myungsung Kim, Sangil Lee, Sung-Phil Kim, Dooyoung Jung

“…The architecture comprised response-generating and procrastination factor–detection algorithms. A pilot randomized controlled trial was conducted with 85 participants (n=37, 44% female; n=48, 56% male) from a university in South Korea. …”
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Challenges and Opportunities of the Human-Centered Design Approach: Case Study Development of an Assistive Device for the Navigation of Persons With Visual Impairment by Mario Andres Chavarria, Luisa María Ortiz-Escobar, Bladimir Bacca-Cortes, Victor Romero-Cano, Isabella Villota, Jhon Kevin Muñoz Peña, José Roberto Londoño Sánchez, Oscar Campo, Silvan Suter, Jhon Jairo Cabrera-López, Maria Fernanda Sanchez Patiño, Eduardo Francisco Caicedo-Bravo, Michael Stein, Samia Hurst, Klaus Schönenberger, Minerva Rivas Velarde

“…The navigation system integrates artificial intelligence algorithms, specifically developed by the research team as part of this work, to enhance its adaptability and responsiveness to diverse environments. …”
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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…ConclusionsThis study evaluates prediction models constructed using various ML algorithms, with results showing that ML demonstrates high performance in ARDS prediction. …”
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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. …”
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Combining methylated RNF180 and SFRP2 plasma biomarkers for noninvasive diagnosis of gastric cancer by Zhihao Dai, Jin Jiang, Qianping Chen, Minghua Bai, Quanquan Sun, Yanru Feng, Dong Liu, Dong Wang, Tong Zhang, Liang Han, Litheng Ng, Jun Zheng, Hao Zou, Wei Mao, Ji Zhu

“…Materials & Methods: A total of 165 healthy individuals, 34 patients with precancerous gastric lesions, and 104 patients with confirmed GC were divided into training and validation sets; methylated RNF180 and SFRP2 were detected in circulating DNA from blood samples. Six models, including those based on logistic regression, Naive Bayes, K-nearest neighbor algorithm, glmnet, neural network, and random forest (RF) were built and validated. …”
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Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods by Pan-Pan Zhang, Xin-Jing Gui, Xin-Jing Gui, Xin-Jing Gui, Xue-Hua Fan, Han-Li, Hai-Yang Li, Xiao-Peng Li, Feng-Yu Dong, Yan-Li Wang, Jing-Yao, Jing-Yao, Jun-Han Shi, Jun-Han Shi, Rui-Xin Liu, Rui-Xin Liu, Rui-Xin Liu, Rui-Xin Liu, Rui-Xin Liu

“…A total of 111 VOCs were detected by HS-GC-IMS, with 101 tentatively identified. …”
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Utility of serum Aspergillus-galactomannan antigen to evaluate the risk of severe acute exacerbation in chronic obstructive pulmonary disease. by Katsuhiro Yoshimura, Yuzo Suzuki, Yusuke Inoue, Koji Nishimoto, Kazutaka Mori, Masato Karayama, Hironao Hozumi, Kazuki Furuhashi, Noriyuki Enomoto, Tomoyuki Fujisawa, Yutaro Nakamura, Naoki Inui, Koushi Yokomura, Shiro Imokawa, Takafumi Suda

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Increased mobilization of mesenchymal stem cells in patients with acute respiratory distress syndrome undergoing extracorporeal membrane oxygenation. by Christian Patry, Thalia Doniga, Franziska Lenz, Tim Viergutz, Christel Weiss, Burkhard Tönshoff, Armin Kalenka, Benito Yard, Jörg Krebs, Thomas Schaible, Grietje Beck, Neysan Rafat

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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. …”
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Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study by Anneleen Dereymaeker, Vladimir Matic, Jan Vervisch, Perumpillichira J. Cherian, Amir H. Ansari, Ofelie De Wel, Paul Govaert, Maarten De Vos, Sabine Van Huffel, Gunnar Naulaers, Katrien Jansen

“…EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. …”
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Enhancing Cardiovascular Risk Prediction with a Simplified Carotid IMT Protocol: Evidence from the IMPROVE Study by Fabrizio Veglia, Anna Maria Malagoni, Mauro Amato, Rona J. Strawbridge, Kai Savonen, Philippe Giral, Antonio Gallo, Matteo Pirro, Bruna Gigante, Per Eriksson, Douwe J. Mulder, Beatrice Frigerio, Daniela Sansaro, Alessio Ravani, Daniela Coggi, Roberta Baetta, Nicolò Capra, Elena Tremoli, Damiano Baldassarre

“…The ability to predict CV events, on top of the SCORE2/SCORE2-OP risk algorithm, was quantified by the time-dependent increase in the receiver operating characteristic (ROC) area under the curve (AUC). …”
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Automated Assessment of the Pulmonary Artery-to-Ascending Aorta Ratio in Fetal Cardiac Ultrasound Screening Using Artificial Intelligence by Rina Aoyama, Masaaki Komatsu, Naoaki Harada, Reina Komatsu, Akira Sakai, Katsuji Takeda, Naoki Teraya, Ken Asada, Syuzo Kaneko, Kazuki Iwamoto, Ryu Matsuoka, Akihiko Sekizawa, Ryuji Hamamoto

“…In total, 315 cases and 20 examiners were included in this study. We used the object-detection software YOLOv7 for the automated extraction of 3VV images and compared three segmentation algorithms: DeepLabv3+, UNet3+, and SegFormer. …”
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Proteomic and metabolomic profiles of plasma-derived Extracellular Vesicles differentiate melanoma patients from healthy controls by SM Bollard, J Howard, C Casalou, BS Kelly, K O'Donnell, G Fenn, J O'Reilly, R Milling, M Shields, M Wilson, A Ajaykumar, K Triana, K Wynne, DJ Tobin, PA Kelly, A McCann, SM Potter

“…Proteomic and Metabolomic Analyses were performed, and machine learning algorithms were used to identify potential proteins and metabolites to differentiate the plasma-derived EVs from melanoma patients of different disease stages. …”
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Factors associated with tuberculosis drug resistance among presumptive multidrug resistance tuberculosis patients identified in a DRTB surveillance study in western Kenya by Albert Okumu, James Orwa, Ruth Sitati, Isaiah Omondi, Ben Odhiambo, Jeremiah Ogoro, George Oballa, Benjamin Ochieng, Steve Wandiga, Collins Ouma

“…We recommend active surveillance on drug resistant TB in these regions to detect drug resistance patterns for rapid disease management.…”
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Genome-wide association mapping of aluminum toxicity tolerance and fine mapping of a candidate gene for Nrat1 in rice. by Yonghong Tao, Yanan Niu, Yun Wang, Tianxiao Chen, Shahzad Amir Naveed, Jian Zhang, Jianlong Xu, Zhikang Li

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Combination of remote sensing with crop modeling using Bayesian inferences to predict irrigated cotton yield by Farzam Moghbel, Forough Fazel, Jonathan Aguilar, Nathan Howell, Juan Enciso

“…A considerably less accurate correlation was detected for fitting the polynomial model (0.4 <RMSE<4.2). …”
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Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference by Yinlei Lei, Min Li, Han Zhang, Yu Deng, Xinyu Dong, Pengyu Chen, Ye Li, Suhua Zhang, Chengtao Li, Shouyu Wang, Ruiyang Tao

“…Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. …”
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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis by Qianqian Zhao, Yijie Li, Chunliu Zhao, Ran Dong, Jiaxin Tian, Ze Zhang, Lin Huang, Jingwen Huang, Junhai Yan, Zhitao Yang, Jiangnan Ruan, Ping Wang, Li Yu, Jieming Qu, Min Zhou

“…Abstract Background The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the urgency advanced predictive tools. …”
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