New Trends in Precision Medicine: A Pilot Study of Pure Light Scattering Analysis as a Useful Tool for Non-Small Cell Lung Cancer (NSCLC) Diagnosis

Rossi, Domenico; Dannhauser, David; Nastri, Bianca Maria; Ballini, Andrea; Fiorelli, Alfonso; Santini, Mario; Netti, Paolo Antonio; Scacco, Salvatore; Marino, Maria Michela; Causa, Filippo; Boccellino, Mariarosaria; Domenico, Marina Di

doi:10.3390/jpm11101023

Cited by 5 publications

(3 citation statements)

References 25 publications

(40 reference statements)

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“…Alzheimer's disease [35][36][37][38][39][40] Autism Spectrum Disorders [41,42] Brain Tumors [43][44][45] Breast Diabetes [73][74][75][76][77][78] Exposure to extremely low frequency waves [79,80] Glioblastoma [81,82] Heart Failure [83][84][85][86][87] Kidney Disease [88,89] Lung Cancer [90][91][92][93][94] Melanoma [95,96] Multiple Sclerosis [97][98][99] Parkinson's Disease [100][101][102][103][104][105][106][107] Prostate Cancer [108,109] Rectal Cancer [110]…”

Section: Topic Referencementioning

confidence: 99%

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Bottrighi,

Pennisi

2023

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Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.

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Section: Topic Referencementioning

confidence: 99%

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Bottrighi,

Pennisi

2023

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“…In fact, recently, biophysical cell information investigations showed promising cell fraction identification potential, especially for sparsely present cells. [4][5][6][7][8][9] The relative cell count, anomalous cell shapes or cytoplasm complexity were found to be useful indicators for dysregulated responses to inflammation stimuli. [10,11] But cells are not homogenous objects, as they are composed of membranes, cytoplasm, nuclei, and various organelles to name the most important contributors for heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Out of distribution detection in deep learning-based scattering pattern classification

Dannhauser,

Netti,

Causa

2023

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI

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Intrinsic biophysical cell properties hold an enormous potential for cell class and state classification in microfluidics, allowing to avoid the need of cost intensive fluorescence labelling. Several methods can accomplish cell identification, while convolutional neural networks show an outstanding performance compared to other state-of-the-art classification methods, regarding accuracy and speed. In fact, neural networks show high performance for known image class prediction but struggles when unknown (out of distribution) image classes need to be identified. In such a scenario no prior knowledge of the unknown cell class can be used for the model training, which inevitably results in image misclassification. In fact, to distinguish unknown cell classes, a neural network must first construct an in-distribution of known images to afterwards detect out of distribution as unknowns, which is also called open-set classification assumption. Ones, a new cell class is identified, the neural network can be retrained with the obtained knowledge to dynamically update its cell class database. This process can be simply repeated for each new detected cell class. We applied this open-set idea to scattering pattern snapshots of different classes of living cells obtained in microfluidics. Our outcome shows a proof-of-concept for openset based convolutional neural network for cell image classification, which can be applied to a wide range of single cell classification approaches to reduce uncertainty in machine learning based technologies.

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“…The machine learning algorithms have been recently incorporated the 2D light scattering cytometry for automatic cancer cell analysis without the bio-labeling of the cells 5,6 . Compared with conventional flow cytometry, the 2D light scattering cytometry can provide unperturbed cellular information that is important for clinical diagnosis [11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Identification of refractive index for label-free single cells based on 2D light scattering technology

Zhang²,

Wei³

et al. 2022

Optics in Health Care and Biomedical Optics XII

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Two dimensional (2D) light scattering technology is an important part of the single cell analysis, which can recognize the differences of inherent physical properties of biological particles. Based on the label-free static cytometry, collimated light from a laser with a wavelength of 532 nm is coupled into a multimode optical fiber. The fiber localizes and illuminates single static biological particles. Single particles are excited and their scattered light is collected. In this paper, the 2D light scattering patterns are obtained from 4 μm and 6 μm polystyrene microspheres (PS, refractive index, 1.59) and silica microspheres (SiO2, refractive index, 1.45). The corresponding simulated scattering patterns are obtained by Mie scattering theory. Then, Fourier analysis of the 2D scattering patterns is performed for measuring the refractive index of single particles. The results indicate that the 2D light scattering technique may provide an efficient, low-cost, and easy-to-use approach to the nondestructive identification and classification of cancer cells at the single cell level.

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New Trends in Precision Medicine: A Pilot Study of Pure Light Scattering Analysis as a Useful Tool for Non-Small Cell Lung Cancer (NSCLC) Diagnosis

Cited by 5 publications

References 25 publications

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Out of distribution detection in deep learning-based scattering pattern classification

Identification of refractive index for label-free single cells based on 2D light scattering technology

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