Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach by Yazeed Alashban

“…CLINICAL SIGNIFICANCE: The proposed two-tier DL algorithm, combining a modified VGG19 model for image classification and YOLOv5-CBAM for lesion detection, can improve the accuracy, efficiency, and reliability of breast cancer screening and diagnosis through innovative artificial intelligence-driven methodologies.…”
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Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. by Lin Lin, Elena Dacal, Nuria Díez, Claudia Carmona, Alexandra Martin Ramirez, Lourdes Barón Argos, David Bermejo-Peláez, Carla Caballero, Daniel Cuadrado, Oscar Darias-Plasencia, Jaime García-Villena, Alexander Bakardjiev, Maria Postigo, Ethan Recalde-Jaramillo, Maria Flores-Chavez, Andrés Santos, María Jesús Ledesma-Carbayo, José M Rubio, Miguel Luengo-Oroz

“…It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. …”
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A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs by Pengyu Chu, Bo Han, Qiang Guo, Yiping Wan, Jingjing Zhang

“…Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. …”
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Dynamic SOFA component scores-based deep learning for short to long-term mortality prediction in sepsis survivors by Juan Wei, Feihong Lin, Tian Jin, Qian Yao, Sheng Wang, Di Feng, Xin Lv, Wen He

“…Comparisons were made with a multilayer perceptron and two machine learning models of random forest and eXtreme Gradient Boosting (XGBoost). …”
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Callback time preference for prescreening visits among Black residents in the Boston area: findings from two randomized controlled trials by Ruth Zeto, Oluwagbemisola Ibikunle, Jingyi Cao, Hannah Col, Dhrumil Patil, Ruth-Alma Turkson-Ocran, Mingyu Zhang, Timothy B. Plante, Stephen P. Juraschek

“…Staff call attempts and participant screening status were logged prospectively. Gender was estimated based on first name, using a published algorithm. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
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Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…For single-image predictions in the three-class classification task of CCM images, the InceptionV3 model achieved a precision of 0.8385, a recall of 0.9083, an F1 score of 0.8720 and an AUC of 0.8769 for predicting DPN+.Conclusions The InceptionV3-based DLA model achieved superior performance compared with traditional convolutional neural network architectures like ResNet and DenseNet, and the Swin transformer model, highlighting its potential for effective DPN screening.…”
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Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data. …”
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Beyond identification of familial hypercholesterolemia: Improving downstream visits and treatments in a large health care system by Harin Lee, Tarun Kadaru, Ruth Schneider, Taylor Triana, Carol Tujardon, Colby Ayers, Mujeeb Basit, Zahid Ahmad, Amit Khera

“…Patients whose PCP was contacted were more likely to have adjustments made to their lipid lowering medication(s) (p = 0.016), be diagnosed with FH (p = 0.025), and have a follow-up visit (p = 0.033). …”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. …”
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MYOPIA PREVALENCE AMONG STUDENTS DURING COVID-19 PANDEMIC. A SYSTEMATIC REVIEW AND META-ANALYSIS by Natasha Hana Savitri, Adinda Sandya Poernomo, Muhammad Bagus Fidiandra1, Eka Candra Setyawan1, Arinda Putri Auna Vanadia1, Bulqis Inas Sakinah1, Lilik Djuari

“…Data retrieval used the PICO method and journal adjustments were selected using the PRISMA algorithm. Data analysis was performed using a random-effects model. …”
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Application of artificial intelligence in the diagnosis and treatment of lacrimal disorders: challenges and opportunities by PENG Xintong, LI Guangyu

“…AI has the ability to provide more precise disease identification and treatment strategies through efficient image analysis, multimodal data fusion, and deep learning algorithms. …”
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The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data by Yue Cui, Yi Qiao, Rongming An, Xuan Pan, Jing Tu

“…Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.…”
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Mitochondrial-Localized Protein Transcript Abundance can Predict the Prognosis of Endometrial Carcinoma: A Retrospective Analysis by Weifei Liang, Hong Lin, Donglin Sun, Minghui Tang

“…Moreover, a nomogram was constructed through the combination of the scoring algorithm and the patient’s clinical features. Conclusions: The scoring algorithm based on mitochondrial gene expression can assist clinicians in predicting the postoperative survival rate of patients, allowing them to devise more precise treatment programs.…”
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Perceived age estimation from facial image and demographic data in young and middle-aged South Korean adults by Ilkoo Ahn, Younghwa Baek, Bok-Nam Seo, Su Eun Lim, Kyoungsik Jung, Ho Seok Kim, Jeongkyun Kim, Sukyung Lee, Siwoo Lee

“…The averaging models of Lasso, XGBoost, and CatBoost showed a mean absolute error of 2.2944, indicating that this algorithm can be used as a screening method for general health status in the population.…”
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Implementation costs and cost-effectiveness of ultraportable chest X-ray with artificial intelligence in active case finding for tuberculosis in Nigeria. by Tushar Garg, Stephen John, Suraj Abdulkarim, Adamu D Ahmed, Beatrice Kirubi, Md Toufiq Rahman, Emperor Ubochioma, Jacob Creswell

“…We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. …”
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TikTok and Sound: Changing the ways of Creating, Promoting, Distributing and Listening to Music by Bojana Radovanović

“…In this article I will explore the ways in which TikTok has made an “aural turn” (Abidin and Kaye 2021), and thus changed and influenced the processes of music-making, music listening and music promotion. …”
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A clinical scoring system to prioritise investigation for tuberculosis among adults attending HIV clinics in South Africa. by Yasmeen Hanifa, Katherine L Fielding, Violet N Chihota, Lungiswa Adonis, Salome Charalambous, Nicola Foster, Alan Karstaedt, Kerrigan McCarthy, Mark P Nicol, Nontobeko T Ndlovu, Edina Sinanovic, Faieza Sahid, Wendy Stevens, Anna Vassall, Gavin J Churchyard, Alison D Grant

“…<h4>Participants</h4>Representative sample of adult HIV clinic attendees; data from participants reporting ≥1 symptom on the WHO screening tool were split 50:50 to derive, then internally validate, a prediction model.…”
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Sweetener identification using transfer learning and attention mechanism by Fanchao Lin, Yuan Ji, Shoujiang Xu

“…Accurate identification of the taste of compounds has helped in the screening and development of new sweeteners. This study proposes a deep learning model for sweetener identification based on transfer learning and attention mechanism. …”
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Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2 by I. Govindharaj, D. Santhakumar, K. Pugazharasi, S. Ravichandran, R. Vijaya Prabhu, J. Raja

“…This approach does not only contribute to glaucoma screening but also can also reveal the benefits of the GANs and transfer learning in medical imaging. • A GAN approach to generate high-quality fundus image datasets in an attempt to minimize dataset differences. • Implemented improved Enhanced Level Set Algorithm for Optic Cup segmentation. • Built on top of the pretrained MobileNetV2 to obtain better results of glaucoma classification.…”
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