Improving routine mental health screening for depression and anxiety in a paediatric lupus clinic: a quality improvement initiative for enhanced mental healthcare by Deborah M Levy, Evelyn Smith, Lawrence Ng, Andrea M Knight, Linda Hiraki, Tala El Tal, Avery Longmore, Audrea Chen, Holly Convery, Dinah Finkelstein, Chetana Kulkarni, Neely Lerman, Karen Leslie, Sharon Lorber, Oscar Mwizerwa, Vandana Rawal, Stephanie Wong, Asha Jeyanathan

“…Statistical process control charts were used to analyse the outcome measure for percentage of screened patients with cSLE. Patient and caregiver satisfaction surveys were conducted at baseline and after screening as a balancing measure.Interventions MH screening workflow with a referral algorithm was developed with stakeholders. …”
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Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data by Da Zhang, Lihong Zhao, Bo Guo, Aihong Guo, Jiangbo Ding, Dongdong Tong, Bingju Wang, Zhangjian Zhou

“…Furthermore, by integrating feature importance values, Shapley values, and local interpretable model-agnostic explanation (LIME) values, we demonstrated that the diagnostic area under the curve (AUC) achieved by our multi-dimensional learning models approached 1, significantly outperforming the diagnostic AUC of single markers derived from the PRIDE database. …”
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A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach by Paola Argiento, Anna D'Agostino, Rossana Castaldo, Monica Franzese, Matteo Mazzola, Ekkehard Grünig, Lavinia Saldamarco, Valeria Valente, Alessandra Schiavo, Erica Maffei, Davide Lepre, Antonio Cittadini, Eduardo Bossone, Michele D'Alto, Luna Gargani, Alberto Maria Marra

“…Conclusions: The PH-targeted ML models could streamline routine screening for PH, facilitating earlier identification and better RHC referrals.…”
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Genetics, sex and the use of platelet‐rich plasma influence the development of arthrofibrosis after anterior cruciate ligament reconstruction by Mikel Sánchez, Izarbe Yarza, Cristina Jorquera, Jose María Aznar, Leonor López deDicastillo, Cristina Valente, Renato Andrade, João Espregueira‐Mendes, David Celorrio, Beatriz Aizpurua, Juan Azofra, Diego Delgado

“…Abstract Purpose To identify genes and patient factors that are related to the development of arthrofibrosis in patients after anterior cruciate ligament (ACL) reconstruction and to develop a prognostic model. Methods The study included patients diagnosed with ACL injury who underwent ACL reconstruction. …”
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Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation by Tatsuki Hasegawa, Hayato Kizaki, Keisho Ikegami, Shungo Imai, Yuki Yanagisawa, Shuntaro Yada, Eiji Aramaki, Satoko Hori

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Economic evaluation of a novel genetic screening test for risk of venous thromboembolism compared with standard of care in women considering combined hormonal contraception in Swit... by Zanfina Ademi, C Simone Sutherland, Matthias Schwenkglenks, Nadine Schur, Joëlle Michaud, Myriam Lingg, Arjun Bhadhuri, Thierry D. Pache, Johannes Bitzer, Pierre Suchon, Valerie Albert, Kurt E. Hersberger, Goranka Tanackovic

“…The risk of having a VTE was derived from the risk algorithm that underpins the PP test. The remaining model inputs relating to population characteristics, costs, health resource use, mortality and utilities were derived from published studies or national sources. …”
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Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields by Chenkai Gao, Shuimiao Liu, Pengnian Wu, Yanli Wang, Ke Wu, Lingyun Li, Jinghui Wang, Shilong Liu, Peimeng Gao, Zhiheng Zhao, Jing Shao, Haolin Yu, Xiaokang Guan, Tongchao Wang, Pengfei Wen

“…</b> Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. …”
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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

“…Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. …”
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Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation by Julia Geppert, Peter Auguste, Asra Asgharzadeh, Hesam Ghiasvand, Mubarak Patel, Anna Brown, Surangi Jayakody, Emma Helm, Dan Todkill, Jason Madan, Chris Stinton, Daniel Gallacher, Sian Taylor-Phillips, Yen-Fu Chen

“…Although more data were available to populate the screening population model, there was substantial uncertainty across all models. …”
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Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization by Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

“…The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901‐0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (PP ConclusionsThis study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. …”
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SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full Blood Examination by C. F. Choukhan, I. Lasri, R. El Hatimi, M. R. Lemnaouar, M. Esghir

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Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci by Jie Lu, Xinhao Lu, Yixiao Wang, Hengdong Zhang, Lei Han, Baoli Zhu, Boshen Wang

“…The SNP loci screened by these models are pivotal in the process of NIHL prediction, which further improves the prediction accuracy of the model. …”
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Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…Extended color components, a novel sensitive dye screening method, and a feature screening method were integrated and applied to enhance pH detection. …”
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Analytical screening of polymorphic variants of 20S proteasome genes when planning a study of pathogenetic effects of modification of NFKB1 post-translational processing by A. V. Meyer, M. V. Ulyanova, D. O. Imekina, A. D. Padyukova, T. A. Tolochko, E. A. Astafieva, M. B. Lavryashina

“…To calculate the genetic distances between populations, we used the methord of comparing the populations by frequencies of polymorphic marker alleles proposed by Ney, the obtained matrices are illustrated by the method of multidimensional scaling in space using Statistica v.8.0.Results. Discussion of the algorithm and results of analytical screening of polymorphic variants of 14 genes (PSMA1-PSMA7, PSMB1–PSMB7) encoding proteasome subunits 20S. …”
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Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations by Chen YT, Chen GJ, Lin YS

“…Fourteen features (including maternal age, nuchal translucency thickness, serum markers, etc.) were input into the twelve machine learning models, along with seven data-balancing algorithms, to explore the risk prediction outcomes. …”
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Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data Based on Machine-Learning Algorithm by Minhuan Hu, Jingshu Wang, Peng Yang, Ping Li, Peng He, Rutian Bi

“…Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. …”
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Personalised Prostate Cancer Diagnosis: Evaluating Biomarker-based Approaches to Reduce Unnecessary Magnetic Resonance Imaging and Biopsy Procedures by Timo F.W. Soeterik, Xiaobo Wu, Roderick C.N. Van den Bergh, Claudia Kesch, Fabio Zattoni, Ugo Falagario, Alberto Martini, Marcin Miszczyk, Vittorio Fasulo, Martina Maggi, Veeru Kasivisvanathan, Pawel Rajwa, Giancarlo Marra, Giorgio Gandaglia, Peter K.F. Chiu

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Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm by Fengjiao Yue, Si Li, Lijuan Wu, Xuerong Chen, Jianhua Zhu

“…This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. …”
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Recent advances in AI-based toxicity prediction for drug discovery by Hyundo Lee, Jisan Kim, Ji-Woon Kim, Yoonji Lee, Yoonji Lee

“…The advent of computational approaches has accelerated a shift toward in silico modeling, virtual screening, and, notably, artificial intelligence (AI) to identify potential toxicities earlier in the pipeline. …”
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Throw out an oligopeptide to catch a protein: Deep learning and natural language processing-screened tripeptide PSP promotes Osteolectin-mediated vascularized bone regeneration by Yu Chen, Long Chen, Jinyang Wu, Xiaofeng Xu, Chengshuai Yang, Yong Zhang, Xinrong Chen, Kaili Lin, Shilei Zhang

“…In summary, our study established a precise and efficient composite model of DL and NLP to screen bioactive peptides, opening an avenue for the development of various peptide-based therapeutic strategies applicable to a broader range of diseases.…”
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