Concetta Esposito
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Concetta Esposito has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Andrea Cusano
Department of Engineering, University of Sannio, Benevento, Italy
1 shared publication - Chiara Verdone
Department of Agricultural Science, Food, Natural Resources and Engineering, University of Foggia, 71100 Foggia, Italy
1 shared publication - Lerina Aversano
University of Foggia
1 shared publication - Luigi Buonaguro
Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - “Fond G. Pascale”, Naples, Italy
1 shared publication - Marco Pisco
Department of Engineering, University of Sannio, Benevento, Italy
1 shared publication - Maria Tagliamonte
Innovative Immunological Models Unit, National Cancer Institute IRCCS - “Fond G. Pascale”, Italy
1 shared publication
Latest Publications
- Open Access
Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.
Open AccessAssessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.