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  1. Article ; Online: Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.

    Mercaldo, Francesco / Brunese, Luca / Martinelli, Fabio / Santone, Antonella / Cesarelli, Mario

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 17

    Abstract: Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, ... ...

    Abstract Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.
    MeSH term(s) Humans ; Brain ; Brain Neoplasms/diagnostic imaging ; Aggression ; Neural Networks, Computer ; Records
    Language English
    Publishing date 2023-09-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23177614
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: The Role of Ultrasound in Chronic Intestinal Diseases in Pediatric Patients.

    Vallone, Gianfranco / Pizzicato, Paolo / Rossi, Eugenio / Brunese, Luca

    Ultraschall in der Medizin (Stuttgart, Germany : 1980)

    2022  Volume 43, Issue 5, Page(s) 436–455

    Abstract: Chronic inflammatory bowel diseases (IBD) are chronic disorders of the gastrointestinal tract, with an increasing incidence in pediatric populations. Ultrasound of the intestinal wall represents the first-line imaging technique in children since it is a ... ...

    Title translation Die Rolle des Ultraschalls bei chronischen Darmerkrankungen von Kindern und Jugendlichen.
    Abstract Chronic inflammatory bowel diseases (IBD) are chronic disorders of the gastrointestinal tract, with an increasing incidence in pediatric populations. Ultrasound of the intestinal wall represents the first-line imaging technique in children since it is a noninvasive method, is free of ionizing radiation, and is inexpensive. Furthermore, the absence of intestinal wall thickening has a good negative predictive value for IBD, which is greater for Crohn's disease than for ulcerative colitis. Ultrasound is used for the diagnosis of disease, for the differential diagnosis in IBD, in the follow-up of known IBD, in the definition of the site and extent of the disease, for the diagnosis of intestinal complications, for the evaluation of disease activity, in the definition of prognostic parameters, and in the post-operative follow-up.
    MeSH term(s) Child ; Colitis, Ulcerative ; Crohn Disease/diagnostic imaging ; Humans ; Inflammatory Bowel Diseases ; Intestines/diagnostic imaging ; Ultrasonography
    Language English
    Publishing date 2022-10-05
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 801064-x
    ISSN 1438-8782 ; 1439-0914 ; 1431-4894 ; 0172-4614
    ISSN (online) 1438-8782
    ISSN 1439-0914 ; 1431-4894 ; 0172-4614
    DOI 10.1055/a-1891-6421
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A novel methodology for head and neck carcinoma treatment stage detection by means of model checking.

    Brunese, Luca / Mercaldo, Francesco / Reginelli, Alfonso / Santone, Antonella

    Artificial intelligence in medicine

    2022  Volume 127, Page(s) 102263

    Abstract: Context: Head and neck cancers are diagnosed at an annual rate of 3% to 7% with respect to the total number of cancers, and 50% to 75% of such new tumours occur in the upper aerodigestive tract.: Purpose: In this paper we propose formal methods based ...

    Abstract Context: Head and neck cancers are diagnosed at an annual rate of 3% to 7% with respect to the total number of cancers, and 50% to 75% of such new tumours occur in the upper aerodigestive tract.
    Purpose: In this paper we propose formal methods based approach aimed to identify the head and neck tumour treatment stage by means of model checking. We exploit a set of radiomic features to model medical imaging as a labelled transition system to verify treatment stage properties.
    Main findings: We experiment the proposed method using a public dataset related to computed tomography images obtained in different treatment stages, reaching an accuracy ranging from 0.924 to 0.978 in treatment stage detection.
    Principal conclusions: The study confirms the effectiveness of the adoption of formal methods in the head and neck carcinoma treatment stage detection to support radiologists and pathologists.
    MeSH term(s) Head and Neck Neoplasms/diagnostic imaging ; Head and Neck Neoplasms/therapy ; Humans ; Retrospective Studies ; Tomography, X-Ray Computed
    Language English
    Publishing date 2022-03-21
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 645179-2
    ISSN 1873-2860 ; 0933-3657
    ISSN (online) 1873-2860
    ISSN 0933-3657
    DOI 10.1016/j.artmed.2022.102263
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Editorial: Radiomics in prostate cancer imaging.

    Brunese, Luca / Martino, Pasquale / Mischi, Massimo / Prasad, Mukesh / Santone, Antonella

    Frontiers in oncology

    2022  Volume 12, Page(s) 1010901

    Language English
    Publishing date 2022-09-23
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2022.1010901
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  5. Article ; Online: Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions.

    Rocca, Aldo / Komici, Klara / Brunese, Maria Chiara / Pacella, Giulia / Avella, Pasquale / Di Benedetto, Chiara / Caiazzo, Corrado / Zappia, Marcello / Brunese, Luca / Vallone, Gianfranco

    La Radiologia medica

    2024  Volume 129, Issue 4, Page(s) 549–557

    Abstract: Liver steatosis is the most common chronic liver disease and affects 10-24% of the general population. As the grade of disease can range from fat infiltration to steatohepatitis and cirrhosis, an early diagnosis is needed to set the most appropriate ... ...

    Abstract Liver steatosis is the most common chronic liver disease and affects 10-24% of the general population. As the grade of disease can range from fat infiltration to steatohepatitis and cirrhosis, an early diagnosis is needed to set the most appropriate therapy. Innovative noninvasive radiological techniques have been developed through MRI and US. MRI-PDFF is the reference standard, but it is not so widely diffused due to its cost. For this reason, ultrasound tools have been validated to study liver parenchyma. The qualitative assessment of the brightness of liver parenchyma has now been supported by quantitative values of attenuation and scattering to make the analysis objective and reproducible. We aim to demonstrate the reliability of quantitative ultrasound in assessing liver fat and to confirm the inter-operator reliability in different respiratory phases. We enrolled 45 patients examined during normal breathing at rest, peak inspiration, peak expiration, and semi-sitting position. The highest inter-operator agreement in both attenuation and scattering parameters was achieved at peak inspiration and peak expiration, followed by semi-sitting position. In conclusion, this technology also allows to monitor uncompliant patients, as it grants high reliability and reproducibility in different body position and respiratory phases.
    MeSH term(s) Humans ; Non-alcoholic Fatty Liver Disease/diagnosis ; Non-alcoholic Fatty Liver Disease/pathology ; Reproducibility of Results ; Liver/diagnostic imaging ; Ultrasonography/methods ; Liver Cirrhosis/pathology ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2024-03-21
    Publishing country Italy
    Document type Journal Article
    ZDB-ID 205751-7
    ISSN 1826-6983 ; 0033-8362
    ISSN (online) 1826-6983
    ISSN 0033-8362
    DOI 10.1007/s11547-024-01786-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Generative Adversarial Networks in Retinal Image Classification

    Francesco Mercaldo / Luca Brunese / Fabio Martinelli / Antonella Santone / Mario Cesarelli

    Applied Sciences, Vol 13, Iss 10433, p

    2023  Volume 10433

    Abstract: The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns ... ...

    Abstract The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns about the challenge of differentiating between fake and real images. This issue holds significant importance, as images play a vital role in various domains, including image recognition and bioimaging classification in the biomedical field. In this paper, we present a method to assess the distinguishability of bioimages generated by a generative adversarial network, specifically using a dataset of retina images. Once the images are generated, we train several supervised machine learning models to determine whether these classifiers can effectively discriminate between real and fake retina images. Our experiments utilize a deep convolutional generative adversarial network, a type of generative adversarial network, and demonstrate that the generated images, although visually imperceptible as fakes, are correctly identified by a classifier with an F-Measure greater than 0.95. While the majority of the generated images are accurately recognized as fake, a few of them are not classified as such and are consequently considered real retina images.
    Keywords generative adversarial network ; deep convolutional generative adversarial network ; biomedical ; retina ; machine learning ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection

    Luca Brunese / Francesco Mercaldo / Alfonso Reginelli / Antonella Santone

    Applied Sciences, Vol 12, Iss 3877, p

    2022  Volume 3877

    Abstract: Background: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In ... ...

    Abstract Background: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In this paper, we propose a method to analyse respiratory sounds in an automatic way. The aim is to show the effectiveness of machine learning techniques in respiratory sound analysis. A feature vector is gathered directly from breath audio and, thus, by exploiting supervised machine learning techniques, we detect if the feature vector is related to a patient affected by a lung disease. Moreover, the proposed method is able to characterise the lung disease in asthma, bronchiectasis, bronchiolitis, chronic obstructive pulmonary disease, pneumonia, and lower or upper respiratory tract infection. Results: A retrospective experimental analysis on 126 patients with 920 recording sessions showed the effectiveness of the proposed method. Conclusion: The experimental analysis demonstrated that it is possible to detect lung disease by exploiting machine learning techniques. We considered several supervised machine learning algorithms, obtaining the most interesting performance with the neural network model, with an F-Measure of 0.983 in lung disease detection and equal to 0.923 in lung disease characterisation, increasing the state-of-the-art performance.
    Keywords lung ; machine learning ; neural network ; classification ; artificial intelligence ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Coronavirus covid-19 detection by means of explainable deep learning.

    Mercaldo, Francesco / Belfiore, Maria Paola / Reginelli, Alfonso / Brunese, Luca / Santone, Antonella

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 462

    Abstract: The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical ... ...

    Abstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.
    MeSH term(s) Humans ; COVID-19/diagnosis ; SARS-CoV-2 ; Deep Learning ; Pneumonia ; Lung Diseases
    Language English
    Publishing date 2023-01-10
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-27697-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Explainability of radiomics through formal methods.

    Varriano, Giulia / Guerriero, Pasquale / Santone, Antonella / Mercaldo, Francesco / Brunese, Luca

    Computer methods and programs in biomedicine

    2022  Volume 220, Page(s) 106824

    Abstract: Background and objective: Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently ... ...

    Abstract Background and objective: Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems.
    Methods: Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences.
    Results: In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach.
    Conclusions: In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.
    MeSH term(s) Artificial Intelligence ; Humans ; Radiology ; Tomography, X-Ray Computed
    Language English
    Publishing date 2022-04-19
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2022.106824
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    Zs.B 521: Show issues Location:
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  10. Article ; Online: Automatic PI-RADS assignment by means of formal methods.

    Brunese, Luca / Brunese, Maria Chiara / Carbone, Mattia / Ciccone, Vincenzo / Mercaldo, Francesco / Santone, Antonella

    La Radiologia medica

    2021  Volume 127, Issue 1, Page(s) 83–89

    Abstract: Introduction and objectives: The Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a ... ...

    Abstract Introduction and objectives: The Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a clinically significant cancer. The aim of this paper is to propose a methodology to automatically mark a magnetic resonance imaging with its related PI-RADS.
    Materials and methods: We collected a dataset from two different institutions composed by DWI ADC MRI for 91 patients marked by expert radiologists with different PI-RADS score. A formal model is generated starting from a prostate magnetic resonance imaging, and a set of properties related to the different PI-RADS scores are formulated with the help of expert radiologists and pathologists.
    Results: Our methodology relies on the adoption of formal methods and radiomic features, and in the experimental analysis, we obtain a specificity and sensitivity equal to 1.
    Q conclusions: The proposed methodology is able to assign the PI-RADS score by analyzing prostate magnetic resonance imaging with a very high accuracy.
    MeSH term(s) Diffusion Magnetic Resonance Imaging/methods ; Humans ; Male ; Patient Acuity ; Prostate/diagnostic imaging ; Prostatic Neoplasms/diagnostic imaging ; Radiology Information Systems/statistics & numerical data ; Reproducibility of Results
    Language English
    Publishing date 2021-11-25
    Publishing country Italy
    Document type Journal Article
    ZDB-ID 205751-7
    ISSN 1826-6983 ; 0033-8362
    ISSN (online) 1826-6983
    ISSN 0033-8362
    DOI 10.1007/s11547-021-01431-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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