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

“…We did not find any studies that directly compared radiologists' performance with and without the help of artificial intelligence in the UK. …”
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Color Evaluation and Sensory Analyses of Beef Subprimals Following Extended Frozen Storage by Ariana D. Roldan, Dale R Woerner, Jerrad F. Legako, Keelyn E. Hanlon, Marcos X Sanchez Plata, Sabrina E. Blandon, Thomas W. Dobbins, Tyson R. Brown

“…Trained sensory analyses were conducted on steaks without display. …”
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Dehazing algorithm for coal mining face dust and fog images based on a semi-supervised network by Meng ZHAO, Yuzhong WEI, Zheng LI, Junming ZHANG, Junda CHEN, Xiaofeng LIU

“…The synthetic data, along with the collected real data, were used to train the semi-supervised network, enhancing the model's adaptability and performance under non-uniform dust-mist conditions in underground coal mines. …”
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Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study by Salvatore Nigliaccio, Enzo Cumbo, Davide Alessio Fontana, Cesare Fabio Valenti, Domenico Tegolo, Antonino Tocco, Pietro Messina, Giuseppe Alessandro Scardina

“…This pilot study sought to evaluate and compare the performance of an automated method, developed using a neural network trained at the University of Palermo, with a traditional manual method for assessing capillary morphology in the oral mucosa. …”
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Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study by Ruifang Hu, Xianping Liu, Yong Zhang, Clement Arthur, Dongguang Qin

“…Optical biopsy methods provided better visualization of lesions; however, not all patients had all three modalities in a single procedure. …”
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Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans by Wenyu Xing, Yanping Yang, Yanan Zhou, Tao Jiang, Yifang Li, Yuanlin Song, Dongni Hou, Dean Ta

“…Conclusions: The proposed easily-trained and high-performance deep learning model provides a fast and effective way for the diagnosis and dynamic monitoring of pulmonary cavity lesions in clinic. …”
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Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images by Bangxin Xiao, Yang Lv, Canjie Peng, Zongjie Wei, Qiao Xv, Fajin Lv, Qing Jiang, Huayun Liu, Feng Li, Yingjie Xv, Quanhao He, Mingzhao Xiao

“…Deep learning features were extracted and visualized using Grad-CAM. Principal Component Analysis reduced features to 64. …”
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Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemor... by Weixiong Zeng, Jiaying Chen, Linling Shen, Genghong Xia, Jiahui Xie, Shuqiong Zheng, Zilong He, Limei Deng, Yaya Guo, Jingjing Yang, Yijun Lv, Genggeng Qin, Weiguo Chen, Jia Yin, Qiheng Wu

“…Additionally, we incorporated the Shapley Additive Explanation (SHAP) method to display key features and visualize the decision-making process of the model for each individual. …”
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U-shaped association between serum chloride and hypertension risk with nadir around 103 mmol/L: insights from regression and interpretable machine learning (XGBoost/SHAP) using NHA... by Shancheng He, Xuemei Zhong, Guangming Chen, Long Li

“…Additionally, to further explore the complex relationship between serum chloride levels and hypertension risk, and to understand the contributions of various features within a high-performance machine learning model, we trained an XGBoost classifier to predict hypertension status and utilized SHAP (SHapley Additive exPlanations) values for interpretation.ResultsA substantial connection was acquired between serum chloride levels and the risk of hypertension. …”
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ML Auditing and Reproducibility: Applying a Core Criteria Catalog to an Early Sepsis Onset Detection System by Markus Schwarz, Ludwig Christian Hinske, Ulrich Mansmann, Fady Albashiti

“…Discussion: The catalog application results are visualized in a radar diagram, allowing an auditor to quickly assess and compare strengths and weaknesses of ML algorithm development or implementation projects. …”
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Differentiation between bipolar disorder and major depressive disorder based on AMPA receptor distribution by Sakiko Tsugawa, Sakiko Tsugawa, Yuichi Kimura, Junichi Chikazoe, Hiroki Abe, Tetsu Arisawa, Mai Hatano, Waki Nakajima, Hiroyuki Uchida, Tomoyuki Miyazaki, Tomoyuki Miyazaki, Yuuki Takada, Akane Sano, Kotaro Nakano, Tsuyoshi Eiro, Akira Suda, Takeshi Asami, Akitoyo Hishimoto, Hideaki Tani, Nobuhiro Nagai, Teruki Koizumi, Shinichiro Nakajima, Shunya Kurokawa, Yohei Ohtani, Kie Takahashi, Yuhei Kikuchi, Taisuke Yatomi, Taisuke Yatomi, Ryo Mitoma, Shunsuke Tamura, Shunsuke Tamura, Shingo Baba, Osamu Togao, Yoji Hirano, Yoji Hirano, Yoji Hirano, Hirotaka Kosaka, Hidehiko Okazawa, Masaru Mimura, Takuya Takahashi, Takuya Takahashi

“…A partial least squares model was trained to predict diagnoses based on AMPAR density, and its performance was evaluated using a leave-one-pair-out cross-validation. …”
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An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images by Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He

“…During inference, anomaly scores were computed to distinguish pathological from normal images. The model’s performance was evaluated on both internal and external datasets, and comparative analyses were conducted against existing anomaly detection methods, including generative adversarial networks (AnoGAN), generate to detect anomaly model (G2D), and discriminatively trained reconstruction anomaly embedding model (DRAEM). …”
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Competing risk and random survival forest models for predicting survival in post-resection elderly stage I–III colorectal cancer patients by Qian Zhang, Rongxuan Xu, Wenchong Zhen, Xueting Bai, Zihan Li, Yixin Zhang, Wei Wu, Zhihan Yao, Xiaofeng Li

“…In addition, we also visualized the Fine-Gray subdistribution hazard model with a nomogram and compared it with the nomogram of the Cox model. …”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…Fathers in the spotlight: parental burden and the effectiveness of a parental skills training for anorexia nervosa in mother–father dyads. …”
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Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation by Joshua Kubach, Tobias Pogarell, Michael Uder, Mario Perl, Marcel Betsch, Mario Pasurka, Stefan Söllner, Rafael Heiss

“…To perform internal validation and quality control, the algorithm results were visualized using Guided Score Class activation maps (GSCAM).The AO44-classification sensitivity over all subclasses was 91%. …”
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Referenceless 4D flow cardiovascular magnetic resonance with deep learning by Chiara Trenti, Erik Ylipää, Tino Ebbers, Carl-Johan Carlhäll, Jan Engvall, Petter Dyverfeldt

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Detection of Scabies: A Systematic Review of Diagnostic Methods by Victor Leung, Mark Miller

“…The accuracy of dermatoscopy, performed by a trained practitioner, was determined; however, the accuracy of other diagnostic tests could not be calculated from the data in the literature. …”
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TRANSPORT FORCE AND THE INDEX OF THE DEVELOPMENT POTENTIAL OF HEAVY TRAFFIC AS NEW INDICATORS OF THE USE OF A TRACTION BUSINESS RESOURCE by Oksana D. Pokrovskaya

“…The indicator "transport force" is proposed, which makes it possible to estimate, together with the load capacity, the average axial load of trains running on one kilometer of the operational length of railways and performing transport work. …”
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CFre: An ABAQUS plug-in for creep-fatigue reliability assessment considering multiple uncertainty sources by Yuan-Ze Tang, Xian-Cheng Zhang, Hang-Hang Gu, Chang-Qi Hong, Shan-Tung Tu, Run-Zi Wang

“…By using the data obtained from FEM, the plug-in trains the surrogate model and completes the reliability assessment and visualization. …”
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Rotifer detection and tracking framework using deep learning for automatic culture systems by Naoto Ienaga, Toshinori Takashi, Hitoko Tamamizu, Kei Terayama

“…In addition, this research will contribute to the development of the field by releasing the trained model and code for visualizing the tracking results, as well as an annotated dataset with over 30,000 instances.…”
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