日期 影响因子
日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:
–

Proposed dense variational autoencoder model integrated with contrastive learning for foot ulcer classification

提出了一种结合对比学习的密集变分自编码器模型用于足部溃疡分类

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-025-34225-7
Shandilya, Gunjan; Gupta, Sheifali; Gupta, Deepali; Juneja, Sapna; Chadaga, Krishnaraj; Nauman, Ali; Al-Masri, Abeer A
发表日期：2026
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Sickle cell disease detection in low-resource conditions using transfer-learning and contrastive-learning coupled with XAI

利用迁移学习和对比学习结合可解释人工智能，在资源匮乏条件下检测镰状细胞病

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-026-35831-9
Patel, Jay; Muralikrishna, H; Chadaga, Krishnaraj; Thalengala, Ananthakrishna; Sampathila, Niranjana
细胞生物学
发表日期：2026
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Endocrine-Taste Crosstalk: A Scoping Review on Thyroid Dysfunction and Its Genetic Links to Taste Receptors With Dysgeusia

内分泌-味觉串扰：甲状腺功能障碍及其与味觉障碍味觉受体遗传联系的范围综述

期刊:International Journal of Endocrinology
影响因子:2.3
doi:10.1155/ije/1156681
Pai, Panchami; Chadaga, Ananya; Natarajan, Srikant; Chandrashekar, Chetana; Rodrigues, Gabriel; Carnelio, Sunitha
信号转导
免疫/内分泌
发表日期：2026
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AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques

AlzStack：利用多种数据平衡技术，通过可解释的人工智能系统预测早发性阿尔茨海默病

期刊:Global Epidemiology
影响因子:
doi:10.1016/j.gloepi.2025.100235
Modali, Venkata Aditi; Pavanya, Manohar; Arjunan, R Vijaya; Cenitta, D; Sampathila, Niranjana; Kamath, Radhika; Chadaga, Krishnaraj
阿尔茨海默症
发表日期：2026
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Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters

利用机器学习和临床参数，通过可解释的人工智能驱动的吸烟预测洞察

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-025-09409-w
Aishwarya, S; Siddalingaswamy, P C; Chadaga, Krishnaraj
发表日期：2025
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Author Correction: An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients

作者更正：一种用于儿科患者阑尾炎检测的可解释且透明的机器学习框架

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-025-86494-x
Chadaga, Krishnaraj; Khanna, Varada; Prabhu, Srikanth; Sampathila, Niranjana; Chadaga, Rajagopala; Umakanth, Shashikiran; Bhat, Devadas; Swathi, K S; Kamath, Radhika
阑尾炎
发表日期：2025
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Detection of breast cancer using machine learning and explainable artificial intelligence

利用机器学习和可解释人工智能检测乳腺癌

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-025-12644-w
Arravalli, Tharunya; Chadaga, Krishnaraj; Muralikrishna, H; Sampathila, Niranjana; Cenitta, D; Chadaga, Rajagopala; Swathi, K S
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes

利用机器学习和可解释人工智能结合血液属性对缺铁性贫血和再生障碍性贫血进行鉴别诊断

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-024-84120-w
Darshan, B S Dhruva; Sampathila, Niranjana; Bairy, G Muralidhar; Prabhu, Srikanth; Belurkar, Sushma; Chadaga, Krishnaraj; Nandish, S
贫血
代谢
发表日期：2025
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A Model of the First Trimester Evaluation of Foetal Movements and Their Outcomes via Explainable Artificial Intelligence: A Multicentric Study

基于可解释人工智能的妊娠早期胎动及其结果评估模型：一项多中心研究

期刊:Healthcare Technology Letters
影响因子:3.3
doi:10.1049/htl2.70014
Pavanya, Manohar; Chadaga, Krishnaraj; J, Vennila; Vasudeva, Akhila; Rao, Bhamini Krishna; Bhat, Shashikala K
发表日期：2025
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An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients

一种用于儿科患者阑尾炎检测的可解释且透明的机器学习框架

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-024-75896-y
Chadaga, Krishnaraj; Khanna, Varada; Prabhu, Srikanth; Sampathila, Niranjana; Chadaga, Rajagopala; Umakanth, Shashikiran; Bhat, Devadas; Swathi, K S; Kamath, Radhika
阑尾炎
发表日期：2024
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