Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients by Ying Wang, Hexiang Wang, Na Li, Siyu Wu, Rongchao Shi, Kui Sun, Ximing Wang

“…Utilizing quantitative analysis of tumor subregions via CT imaging holds promise in identifying high-risk populations. Developing and evaluating the performance of an unsupervised clustering algorithm-based intratumoral subregion radiomics model for distinguishing between bladder muscle invasion and AJCC stage. …”
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Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration by Qiang Liu, Hua Zhang, Heng Xiao, Ao Ren, Ying Cai, Rui Liao, Huarong Yu, Zhongjun Wu, Zuotian Huang

“…Cell experiments were performed to evaluate the function of R-spondin 3 (RSPO3) in HCC cells. …”
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Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework by Yufeng Ke, Jiale Du, Shuang Liu, Dong Ming

“…This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). …”
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Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma by Shiyan Song, Wenfei Ge, Xiaochen Qi, Xiangyu Che, Qifei Wang, Guangzhen Wu

“…Receiver operating characteristic (ROC) curve was used to evaluate the performance of the model. Draw decision curve analysis (DCA) to evaluate the clinical personalized medical value of the predictive model. …”
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Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path by Allan Dos S. Braga, Hugo A. O. Da Cruz, Leslye E. C. Eras, Jasmine P. L. Araujo, Miercio C. A. Neto, Diego K. N. Silva, Gervasio P. S. Cavalcante

“…The ANN is a Multilayer Perceptron Network (MLP) that uses the Levenberg-Marquardt training algorithm and cross-validation method. The NFS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) that uses the model Sugeno. …”
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Integrated single cell and bulk RNA sequencing analyses reveal the impact of tryptophan metabolism on prognosis and immunotherapy in colon cancer by Yanyan Hu, Ximo Xu, Hao Zhong, Chengshen Ding, Sen zhang, Wei Qin, Enkui Zhang, Duohuo Shu, Mengqin Yu, Naijipu Abuduaini, Xiao Yang, Bo Feng, Jianwen Li

“…The Oncopredict algorithm facilitated the identification of sensitive chemotherapeutic agents, while the immune escape score was employed to evaluate the immunotherapy response across risk groups. …”
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Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application by Jinyoung Kim, Min-Hee Kim, Dong-Jun Lim, Hankyeol Lee, Jae Jun Lee, Hyuk-Sang Kwon, Mee Kyoung Kim, Ki-Ho Song, Tae-Jung Kim, So Lyung Jung, Yong Oh Lee, Ki-Hyun Baek

“…Background This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. …”
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Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study by Shuhei Toba, Yoshihide Mitani, Yusuke Sugitani, Yusuke Sugitani, Hiroyuki Ohashi, Hirofumi Sawada, Mami Takeoka, Naoki Tsuboya, Kazunobu Ohya, Noriko Yodoya, Takato Yamasaki, Yuki Nakayama, Hisato Ito, Masahiro Hirayama, Motoshi Takao

“…Eighty-three percent of the patients were assigned to a training group, and 17% to a test group. The diagnostic performance of the model and a conventional algorithm (ECAPS12C, Nihon Kohden, Japan) for classifying abnormal electrocardiograms were evaluated using the cross-tabulation, McNemar's test, and decision curve analysis.ResultsWe included 1,842 ECGs performed in 1,062 patients in this study, and 310 electrocardiograms performed in 177 patients were included in the test group. …”
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Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing by Hoang-Anh T. Vo, Sang Nguyen, Ai-Quynh T. Tran, Han Nguyen, Hai Bich Ho, Philip W. Fowler, Timothy M. Walker, Thuy Thi Nguyen

“…Automated Mycobacterial Growth Detection Algorithm (AMyGDA) is a software package that uses image processing techniques to read plates, but struggles with plates that exhibit low growth or images of low quality. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…However, no predictive models for BSH have been developed using machine learning algorithms.<h4>Objective</h4>To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH.…”
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MSF-GhostNet: Computationally Efficient YOLO for Detecting Drones in Low-Light Conditions by Maham Misbah, Misha Urooj Khan, Zeeshan Kaleem, Ali Muqaibel, Muhamad Zeshan Alam, Ran Liu, Chau Yuen

“…The proposed solution also outperformed several other state-of-the-art algorithms exists in the literature.…”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…Pore pressure is used as the output level to generate data-driven models. 70% of the dataset is used for training the machine learning models, while the remaining 30% is reserved for testing the models to evaluate their performance and generalization capability. …”
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A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images by B.E. Malyugin, S.N. Sakhnov, L.E. Axenova, K.D. Axenov, E.V. Kozina, V.V. Vronskaya, V.V. Myasnikova

“…To evaluate the effectiveness of the proposed model, the Dice coefficient (DSC) was used. …”
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Performance of ChatGPT-3.5 and ChatGPT-4 in the field of specialist medical knowledge on National Specialization Exam in neurosurgery by Maciej Laskowski, Marcin Ciekalski, Marcin Laskowski, Bartłomiej Błaszczyk, Marcin Setlak, Piotr Paździora, Adam Rudnik

“…Conclusions: ChatGPT-4 shows improved accuracy over ChatGPT-3.5, likely due to advanced algorithms and a broader training dataset, highlighting its better grasp of complex neurosurgical concepts.…”
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Modelling soil prokaryotic traits across environments with the trait sequence database ampliconTraits and the R package MicEnvMod by Jonathan Donhauser, Anna Doménech-Pascual, Xingguo Han, Karen Jordaan, Jean-Baptiste Ramond, Aline Frossard, Anna M. Romaní, Anders Priemé

“…We created the trait sequence database ampliconTraits, constructed by cross-mapping species from a phenotypic trait database to the SILVA sequence database and formatted to enable seamless classification of environmental sequences using the SINAPS algorithm. The R package MicEnvMod enables modelling of trait – environment relationships, combining the strengths of different model types and integrating an approach to evaluate the models' predictive performance in a single framework. …”
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SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation, and acute dermal toxicity by Nikoletta-Maria Koutroumpa, Dimitra-Danai Varsou, Panagiotis D. Kolokathis, Maria Antoniou, Konstantinos D. Papavasileiou, Eleni Papadopoulou, Anastasios G. Papadiamantis, Andreas Tsoumanis, Georgia Melagraki, Milica Velimirovic, Antreas Afantitis

“…Different molecular representations for skin toxicity-related endpoints were first evaluated using three machine learning algorithms (Random Forest, Support Vector Machine, and k-Nearest Neighbors), then combined into a unified input space for training a fully connected neural network (FCNN). …”
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Different artificial neural networks for predicting burnout risk in Italian anesthesiologists by Marco Cascella, Alessandro Simonini, Sergio Coluccia, Elena Giovanna Bignami, Gilberto Fiore, Emiliano Petrucci, Alessandro Vergallo, Giacomo Sollecchia, Franco Marinangeli, Roberto Pedone, Alessandro Vittori

“…Methods A dataset (300 Italian anesthesiologists) comprising workplace stressors, psychological well-being indicators, and demographic variables was used to train DNN models. Model performance was measured using standard evaluation metrics, including accuracy, precision, recall, and F1 score. …”
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Enhancing Hajj and Umrah Services Through Predictive Social Media Classification by Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna, Faisal Jamil, Mehdhar S. A. M. Al-Gaashani, Soha Alhelaly, Ahmed Aziz, Ammar Muthanna

“…The labeled posts are subsequently used to train a deep learning model. By incorporating a service-level score algorithm based on the TextBlob NLP library, each post is accurately classified and utilized as a feature in a supervised machine-learning classification system. …”
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Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data by Heyi Guo, Sornkitja Boonprong, Shaohua Wang, Zhidong Zhang, Wei Liang, Min Xu, Xinwei Yang, Kaimin Wang, Jingbo Li, Xiaotong Gao, Yujie Yang, Ruichen Hu, Yu Zhang, Chunxiang Cao

“…The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…This proof-of-concept study represents a first step towards the use of STIED and DL algorithms in the routine clinical MEG evaluation of epilepsy.…”
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