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.

Parkinson’s disease is one of the most common diseases of the nervous system, which although typically develops after age 50, in some cases it also affects younger individuals. Its diagnosis is mainly based on the analysis of symptoms, it affects movement, coordination, and muscle control. Doctors generally analyse tremors, stiffness, slow movement, and difficulty walking as symptoms of the disease. Furthermore, changes in the voice are very common in patients with Parkinson’s disease, although such changes are not easily noticeable at an early stage. The disease can change your breathing, tone of voice, or lower the volume by up to 10 decibels. Therefore, speech recognition can introduce a new methodology of investigation in the diagnosis and monitoring of Parkinson’s disease. This study aims at the development of a model based on the use of Neural Networks for the diagnosis of the disease on voice recordings of different nature, made by both healthy and sick patients. In particular, three types of networks were used: Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) with one and two dimensions. The results are very satisfactory, highlighting the excellent performance of the LSTM with an F-Score of 97%. These results are very encouraging, suggesting that the use of the proposed approach can improve the Parkinson’s diagnostic, making it less costly in terms of time and effort.

Heart disease is today the leading cause of death in the world. The early diagnosis can significantly reduce the risk of death and can be useful to drive successful treatment. However, the early diagnosis requires continuous monitoring of a large set of clinical and lifestyle indicators.This is the reason why there is an increasing number of studies aimed to adopt machine learning to predict heart disease starting from the analysis of the vast range of clinical data that we can collect today thanks to the advent of patients’ digital folders. This work investigates the adoption of a large set of machine learning and deep learning classifiers to predict heart disease from the data gathered by a proposed feature model. The study also proposes hyperparameters optimizations aimed to improve the performance of the adopted classifiers. The evaluation is performed on a real dataset and the obtained results show good performance.