| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Shobha Mandal, Subhadeep Chakraborty, Muhammad Ayaz Tariq, Kamran Ali, Zenia Elavia, Misbah Kamal Khan, Diana Baltodano Garcia, Sofia Ali, Jubran Al Hooti, Divyanshi Vijay Kuma. Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review. Cureus. vol 16. issue 8. 2024-09-05. PMID:39233936. |
dl models such as the brain tumor classification model and the inception-resnet v2, or hybrid techniques that enhance these functions and combine dl networks with support vector machine and k-nearest neighbors, identify tumor phenotypes and brain metastases, allowing real-time decision-making and enhancing preoperative planning. |
2024-09-05 |
2024-09-08 |
Not clear |
| Roberto HolgadoCuadrado, Carmen PlazaSeco, Lisandro Lovisolo, Manuel BlancoVelasc. A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification. IEEE transactions on bio-medical engineering. vol PP. 2024-09-04. PMID:39231059. |
in this work, we explore deep learning (dl) models in the same database to design architectures that provide explainability of the decision making process. |
2024-09-04 |
2024-09-08 |
Not clear |
| Hamed Alqahtani, Ghadah Aldehim, Nuha Alruwais, Mohammed Assiri, Amani A Alneil, Abdullah Mohame. Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model. Heliyon. vol 10. issue 16. 2024-09-03. PMID:39224246. |
recently, deep learning (dl) models can be used to achieve enhanced ecg recognition accuracy in the healthcare decision making system. |
2024-09-03 |
2024-09-05 |
Not clear |
| Phuvamin Suriyaamporn, Boonnada Pamornpathomkul, Prasopchai Patrojanasophon, Tanasait Ngawhirunpat, Theerasak Rojanarata, Praneet Opanasopi. The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review. AAPS PharmSciTech. vol 25. issue 6. 2024-08-15. PMID:39147952. |
currently, artificial intelligence (ai), machine learning (ml), and deep learning (dl) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. |
2024-08-15 |
2024-08-18 |
Not clear |
| Maria Tariq, Usman Ali, Sagheer Abbas, Shahzad Hassan, Rizwan Ali Naqvi, Muhammad Adnan Khan, Daesik Jeon. Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI. Frontiers in plant science. vol 15. 2024-07-11. PMID:38988642. |
despite the efficacy of dl models, they often face challenges related to the explainability of their decision-making processes. |
2024-07-11 |
2024-07-13 |
Not clear |
| Khadijeh Moulaei, Mohammad Reza Afrash, Mohammad Parvin, Shahin Shadnia, Mitra Rahimi, Babak Mostafazadeh, Peyman Erfan Talab Evini, Babak Sabet, Seyed Mohammad Vahabi, Amirali Soheili, Mobin Fathy, Arya Kazemi, Sina Khani, Seyed Mohammad Mortazavi, Sayed Masoud Hossein. Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models. Scientific reports. vol 14. issue 1. 2024-07-08. PMID:38977750. |
ml models, particularly rf and xgb, outperform dl counterparts, underscoring their potential for clinical decision-making. |
2024-07-08 |
2024-07-12 |
Not clear |
| Soobin Kim, Eunhee Lee, Hyoun-Tae Hwang, JongCheol Pyo, Daeun Yun, Sang-Soo Baek, Kyung Hwa Ch. Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models. Water research X. vol 23. 2024-06-14. PMID:38872710. |
ultimately, the proposed dl framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds. |
2024-06-14 |
2024-06-16 |
Not clear |
| Yu Gao, Xue Yang, Hongjun Li, Da-Wei Din. A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging. International journal of medical informatics. vol 189. 2024-06-08. PMID:38851131. |
however, these dl models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability. |
2024-06-08 |
2024-06-11 |
Not clear |
| Zongjie Wei, Yingjie Xv, Huayun Liu, Yang Li, Siwen Yin, Yongpeng Xie, Yong Chen, Fajin Lv, Qing Jiang, Feng Li, Mingzhao Xia. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. International journal of surgery (London, England). vol 110. issue 5. 2024-05-15. PMID:38349205. |
postoperative survival stratification based on radiomics and deep learning (dl) algorithms may be useful for treatment decision-making and follow-up management. |
2024-05-15 |
2024-05-27 |
Not clear |
| Hongzhi Liu, Xiaoyao Wang, Xinqiu Song, Bing Han, Chuiqing Li, Fuzhou Du, Hongmei Zhan. A multiview deep learning-based prediction pipeline augmented with confident learning can improve performance in determining knee arthroplasty candidates. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA. 2024-05-07. PMID:38713857. |
deep learning (dl) techniques can build prediction models for treatment decision-making. |
2024-05-07 |
2024-05-27 |
Not clear |
| Viswan Vimbi, Noushath Shaffi, Mufti Mahmu. Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection. Brain informatics. vol 11. issue 1. 2024-04-05. PMID:38578524. |
explainable artificial intelligence (xai) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ml) and deep learning (dl) models. |
2024-04-05 |
2024-04-07 |
Not clear |
| Myeong Seong Bak, Haney Park, Heera Yoon, Geehoon Chung, Hyunjin Shin, Soonho Shin, Tai Wan Kim, Kyungjoon Lee, U Valentin Nägerl, Sang Jeong Kim, Sun Kwang Ki. Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features. Frontiers in molecular neuroscience. vol 17. 2024-03-07. PMID:38450042. |
however, dl operate like a "black box", where their decision-making process is not transparent and is difficult to understand, which is especially evident when our dl model classifies different states of pain based on cellular calcium signals. |
2024-03-07 |
2024-03-09 |
mouse |
| Mohammad Hossein Sadeghi, Sedigheh Sina, Hamid Omidi, Amir Hossein Farshchitabrizi, Mehrosadat Alav. Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Polish journal of radiology. vol 89. 2024-02-19. PMID:38371888. |
the integration of dl into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. |
2024-02-19 |
2024-02-21 |
Not clear |
| Mumu Aktar, Yiming Xiao, Ali K Z Tehrani, Donatella Tampieri, Hassan Rivaz, Marta Kersten-Oerte. SCANED: Siamese collateral assessment network for evaluation of collaterals from ischemic damage. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. vol 113. 2024-02-16. PMID:38364600. |
research in this area focuses on automated collateral assessment using deep learning (dl) methods to expedite decision-making processes and enhance accuracy. |
2024-02-16 |
2024-02-19 |
Not clear |
| Md Ariful Islam, Md Ziaul Hasan Majumder, Md Alomgeer Hussein, Khondoker Murad Hossain, Md Sohel Mia. A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets. Heliyon. vol 10. issue 3. 2024-02-15. PMID:38356538. |
the study also evaluates the effectiveness of various ml and dl algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. |
2024-02-15 |
2024-02-17 |
Not clear |
| Claudio Urrea, John Kern, Ricardo Navarret. Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture. Sensors (Basel, Switzerland). vol 23. issue 24. 2023-12-23. PMID:38139492. |
the great application potential of this system is underscored, as it can be employed, for example, in deep learning (dl) for pattern recognition or agent decision-making trained by reinforcement learning, etc. |
2023-12-23 |
2023-12-25 |
Not clear |
| Sagheer Khan, Aaesha Alzaabi, Tharmalingam Ratnarajah, Tughrul Arsla. Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model. Computers in biology and medicine. vol 168. 2023-12-07. PMID:38061156. |
large-scale implementation of dt technology requires extensive patient data for accurate monitoring and decision-making with machine learning (ml) and deep learning (dl). |
2023-12-07 |
2023-12-17 |
human |
| Lise Wei, Dipesh Niraula, Evan D H Gates, Jie Fu, Yi Luo, Matthew J Nyflot, Stephen R Bowen, Issam M El Naqa, Sunan Cu. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. The British journal of radiology. vol 96. issue 1150. 2023-11-27. PMID:37660402. |
artificial intelligence (ai) including machine learning (ml) and deep learning (dl) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. |
2023-11-27 |
2023-11-29 |
Not clear |
| Neha D Shetty, Rajasbala Dhande, Bhavik S Unadkat, Pratapsingh Pariha. A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging. Cureus. vol 15. issue 9. 2023-10-24. PMID:37868582. |
the studies showed an accuracy of 72.5% to 100% indicating that dl mri holds an equivalent performance as humans in decision-making and management of knee injuries. |
2023-10-24 |
2023-11-08 |
Not clear |
| S R Menaka, M Prakash, S Neelakandan, Arun Radhakrishna. A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition. Scientific reports. vol 13. issue 1. 2023-10-19. PMID:37857665. |
sensor data analysis for human activity recognition using conventional algorithms and deep learning (dl) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. |
2023-10-19 |
2023-11-08 |
human |