Article
Computer Science, Artificial Intelligence
Ahmed S. Eltrass, Mazhar B. Tayel, Abeer Ammar
Summary: The study proposes a novel hybrid approach combining deep neural networks with linear and nonlinear features extracted from ECG and HRV to enhance the performance of ECG diagnosis. By optimizing deep learning features and aggregating ECG features and HRV measures effectively, the system outperforms other state-of-the-art systems in diagnosing various heart disorders.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematics
Adel A. A. Ahmed, Waleed Ali, Talal A. A. Abdullah, Sharaf J. J. Malebary
Summary: Blood circulation relies on electrical activation, and any disruption to the heart's propagating wave can cause arrhythmias. Electrocardiograms (ECG) are commonly used for diagnosing arrhythmias, but their susceptibility to noise and the randomness of arrhythmic events can lead to misdiagnosis. This study proposes a deep learning model, specifically a one-dimensional convolutional neural network (1D-CNN), to address these limitations and achieve accurate and automatic classification of cardiac arrhythmias.
Article
Computer Science, Information Systems
Majid Sepahvand, Fardin Abdali-Mohammadi
Summary: This paper proposes a method to bridge the gap between arrhythmia classification models using multi-lead ECG signals and those using single-lead ECG signals through knowledge distillation. The results show that the method successfully compresses the model size while maintaining a high level of accuracy.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Shengnan Hao, Hang Xu, Hongyu Ji, Zhiwu Wang, Li Zhao, Zhanlin Ji, Ivan Ganchev
Summary: Electrocardiograms (ECG) are crucial for diagnosing cardiovascular diseases, but manual diagnosis is time-consuming due to the large volume of patient data. Therefore, intelligent automatic ECG signal classification is important for addressing the shortage of medical resources. This study proposes a novel model called G2-ResNeXt for inter-patient heartbeat classification, which enhances feature extraction and classification of ECG signals. Experimental results on the MIT-BIH arrhythmia database show that the proposed model outperforms state-of-the-art models, achieving an overall accuracy of 96.16% and high sensitivity and precision for different types of heartbeat abnormalities.
Article
Chemistry, Multidisciplinary
Kalaivani Rathakrishnan, Seung-Nam Min, Se Jin Park
Summary: Stroke is a major cause of death and neurological disorders among elderly individuals. This study focused on analyzing ECG signals to diagnose stroke, finding a correlation between ECG changes and autonomic dysfunction in stroke patients. Risk factors such as age, male sex, and dyslipidemia were identified as statistically significant in relation to stroke. The k-nearest neighbors (KNN) model showed the highest classification accuracy compared to other machine learning models.
APPLIED SCIENCES-BASEL
(2021)
Article
Biology
Yuzhen Qin, Li Sun, Hui Chen, Wenming Yang, Wei-Qiang Zhang, Jintao Fei, Guijin Wang
Summary: The aim of this study is to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification by transferring disease knowledge from multi-lead ECG to a single-lead ECG interpretation model using a teacher-student approach. The study presents a new method called Contrastive Lead-information Transferring (CLT) and modifies Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to facilitate the transfer of disease information between different views of ECG. The experiments demonstrate significant improvements in diagnostic performance for single-lead ECG.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Information Systems
Hui Yang, Zhiqiang Wei
Summary: In this study, a novel ensemble classification algorithm based on ECG morphological features is proposed for accurate detection of heart ventricular and atrial abnormalities. The method achieved an overall accuracy of 98.68% on fifteen heartbeat types and outperformed component classification algorithms and recent peer works.
Article
Agriculture, Dairy & Animal Science
Persephone McCrae, Hannah Spong, Nadia Golestani, Amin Mahnam, Yana Bashura, Wendy Pearson
Summary: Electrocardiography and heart rate variability are important for assessing equine cardiovascular health and fitness. This study validated a user-friendly smart textile system as a reliable alternative to the gold-standard telemetric device. Simultaneous ECGs were obtained using both devices during rest and submaximal exercise, and no differences were observed in heart rate or heart rate variability metrics.
Article
Engineering, Biomedical
Mohamed Benouis, Lotfi Mostefai, Nicholas Costen, Meryem Regouid
Summary: An enhanced version of 1D local binary pattern is proposed for extracting relevant features for ECG-based human recognition. The use of a one-dimensional local difference pattern operator helps in reducing local and global variation in ECG signals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Biology
Quanan Yang, Lang Zou, Keming Wei, Guanzheng Liu
Summary: This study proposed a new method for detecting obstructive sleep apnea (OSA) using a one-dimensional squeeze-and-excitation (SE) residual group network to thoroughly extract the complementary information between heart rate variability (HRV) and ECG-derived respiration (EDR). The method showed higher accuracy, sensitivity, and specificity compared to existing methods during testing.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Electrical & Electronic
V. Nivitha Varghees, Hua Cao, Laurent Peyrodie
Summary: This article presents a straightforward method for R peak detection and ECG denoising based on variational mode decomposition, mode selection, first-order derivative, Shannon energy-based nonlinear amplification, Hilbert transform, and positive zero-crossing point. The method successfully suppresses various noises and artifacts in the ECG signal and accurately extracts ECG parameters in ambulatory conditions. The experimental results demonstrate the effectiveness of the proposed method in R peak detection and noise removal.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Biomedical
Yi Zhang, Jizheng Yi, Aibin Chen, Le Cheng
Summary: This study proposes a two-way multiplex convolutional neural network based on time-frequency features to classify different cardiac rhythms and arrhythmias with excellent performance in experiments.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Yogender Aggarwal, Joyani Das, Papiya Mitra Mazumder, Rohit Kumar, Rakesh Kumar Sinha
Summary: This study presents an alternative diagnostic system for diabetes based on heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM). The system achieved a classification accuracy of 96.2%, showcasing its potential for accurate diagnosis of diabetic conditions.
PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE
(2021)
Review
Physiology
Tibor Stracina, Marina Ronzhina, Richard Redina, Marie Novakova
Summary: This paper provides an overview of the possibilities of ECG recordings in research and clinical practice, discussing the advantages and disadvantages of various approaches and summarizing the potential for advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques, which offer promising prospects in a wide range of clinical applications. Studying the electrical activity of the heart remains highly important for both experimental and clinical cardiology.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Medicine, General & Internal
Shay Perek, Udi Nussinovitch, Reut Cohen, Yori Gidron, Ayelet Raz-Pasteur
Summary: The prognosis of myocarditis varies greatly, so it is crucial to identify new prognostic factors. The prognostic role of ultra-short heart-rate variability (HRV) in myocarditis is still unknown. In a retrospective study, clinical, laboratory, and HRV parameters were assessed as predictors of severe short-term complications in adult patients with clinically suspected myocarditis. It was found that RMSSD may be a prognostic indicator in myocarditis.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Radiology, Nuclear Medicine & Medical Imaging
Federico Bruno, Domenico Albano, Andrea Agostini, Massimo Benenati, Roberto Cannella, Damiano Caruso, Michaela Cellina, Diletta Cozzi, Ginevra Danti, Federica De Muzio, Francesco Gentili, Giuliana Giacobbe, Salvatore Gitto, Giulia Grazzini, Irene Grazzini, Carmelo Messina, Anna Palmisano, Pierpaolo Palumbo, Alessandra Bruno, Francesca Grassi, Roberta Grassi, Roberta Fusco, Vincenza Granata, Andrea Giovagnoni, Vittorio Miele, Antonio Barile
Summary: Metabolic and overload disorders are rare but important diseases that affect different organs and tissues. Imaging plays a crucial role in early detection and accurate diagnosis, especially in specific organs involved in metabolic pathways. MRI is particularly useful due to its multiparametric properties, but advanced imaging techniques may also be required for accurate characterization and quantification. This review aims to describe the various alterations resulting from these disorders and their imaging findings.
JAPANESE JOURNAL OF RADIOLOGY
(2023)
Review
Oncology
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola, Roberta Galdiero, Nicola Maggialetti, Lucrezia Silvestro, Mario De Bellis, Elena Di Girolamo, Giulia Grazzini, Giuditta Chiti, Maria Chiara Brunese, Andrea Belli, Renato Patrone, Raffaele Palaia, Antonio Avallone, Antonella Petrillo, Francesco Izzo
Summary: Pancreatic cancer is one of the deadliest cancers, and late diagnosis is the main reason for its high mortality rate. Surgical resection is the only curative treatment, so early diagnosis is crucial for improving survival. Therefore, it is appropriate to stratify patients based on familial and genetic risk and develop screening protocols using minimally invasive diagnostic tools.
Review
Medicine, General & Internal
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola, Igino Simonetti, Carmine Picone, Ester Simeone, Lucia Festino, Vito Vanella, Maria Grazia Vitale, Agnese Montanino, Alessandro Morabito, Francesco Izzo, Paolo Antonio Ascierto, Antonella Petrillo
Summary: Immunotherapy is a significant change in oncological treatment, but only a minority of patients benefit from it. The efficacy of immunotherapy is affected by factors such as genetic features and intra-tumor heterogeneity. Classic imaging assessment methods like CT or MRI have limited role in immunotherapy due to different response patterns and the need to assess immunotherapy-related toxic effects promptly.
Review
Medicine, General & Internal
Vincenza Granata, Roberta Fusco, Valeria D'Alessio, Igino Simonetti, Francesca Grassi, Lucrezia Silvestro, Raffaele Palaia, Andrea Belli, Renato Patrone, Mauro Piccirillo, Francesco Izzo
Summary: The aim of this study was to analyze the use of Electrochemotherapy (ECT) in treating primary and secondary liver tumors in different locations and with different histologies. Other Local Ablative Therapies (LAT) were also discussed. The analysis of these papers shows that ECT is safe and effective for treating large lesions, regardless of their histology. Compared to other thermal ablation techniques, ECT performs better in lesions larger than 6 cm and can be safely used for lesions located near vital structures. ECT spares vessels and bile ducts, can be repeated, and can be performed between chemotherapeutic cycles.
Review
Health Care Sciences & Services
Michela Gabelloni, Lorenzo Faggioni, Roberta Fusco, Igino Simonetti, Federica De Muzio, Giuliana Giacobbe, Alessandra Borgheresi, Federico Bruno, Diletta Cozzi, Francesca Grassi, Mariano Scaglione, Andrea Giovagnoni, Antonio Barile, Vittorio Miele, Nicoletta Gandolfo, Vincenza Granata
Summary: Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics, an active research field aimed at extracting quantitative data from diagnostic images, has potential applications in lesion characterization, treatment planning, and prognostic assessment of patients with LM. This article provides a systematic review of the literature to illustrate the current applications, strengths, and weaknesses of radiomics in this field.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Review
Health Care Sciences & Services
Carmen Cutolo, Roberta Fusco, Igino Simonetti, Federica De Muzio, Francesca Grassi, Piero Trovato, Pierpaolo Palumbo, Federico Bruno, Nicola Maggialetti, Alessandra Borgheresi, Alessandra Bruno, Giuditta Chiti, Eleonora Bicci, Maria Chiara Brunese, Andrea Giovagnoni, Vittorio Miele, Antonio Barile, Francesco Izzo, Vincenza Granata
Summary: Liver resection is the most effective treatment for primary liver malignancies and metastatic disease. The type of resection depends on various factors, including the type of malignancy, tumor size, and relation with blood and biliary vessels. Imaging, such as ultrasonography, computed tomography, and magnetic resonance imaging, plays a critical role in postoperative assessment and diagnosing complications.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Review
Biology
Vincenza Granata, Roberta Fusco, Federica De Muzio, Carmen Cutolo, Francesca Grassi, Maria Chiara Brunese, Igino Simonetti, Orlando Catalano, Michela Gabelloni, Silvia Pradella, Ginevra Danti, Federica Flammia, Alessandra Borgheresi, Andrea Agostini, Federico Bruno, Pierpaolo Palumbo, Alessandro Ottaiano, Francesco Izzo, Andrea Giovagnoni, Antonio Barile, Nicoletta Gandolfo, Vittorio Miele
Summary: The only cure for intrahepatic cholangiocarcinoma (iCCA) is surgical resection, and early diagnosis is crucial for improving survival. Artificial Intelligence models can help assess high-risk patients, leading to better diagnosis. Therefore, identifying high-risk patients and utilizing non-invasive screening methods are important.
Review
Radiology, Nuclear Medicine & Medical Imaging
Antonio Galluzzo, Sofia Boccioli, Ginevra Danti, Federica De Muzio, Michela Gabelloni, Roberta Fusco, Alessandra Borgheresi, Vincenza Granata, Andrea Giovagnoni, Nicoletta Gandolfo, Vittorio Miele
Summary: Gastrointestinal stromal tumours, originating from Cajal cells, are rare neoplasms in the gastroenteric tract. Diagnosis is mainly done through endoscopy, echoendoscopy, computed tomography, magnetic resonance imaging, and positron emission tomography. Radiomics, an emerging technique, can extract invisible medical imaging information and convert it into quantitative data, improving diagnosis, treatment, and prognosis of these tumors.
JAPANESE JOURNAL OF RADIOLOGY
(2023)
Review
Oncology
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola, Roberta Galdiero, Nicola Maggialetti, Renato Patrone, Alessandro Ottaiano, Guglielmo Nasti, Lucrezia Silvestro, Antonio Cassata, Francesca Grassi, Antonio Avallone, Francesco Izzo, Antonella Petrillo
Summary: In this narrative review, the role of radiomics in assessing prognostic features for liver metastases patients is discussed. Radiomics analysis allows the assessment of textural characteristics in radiological images, which can provide biological data without invasive procedures. However, issues such as poor standardization, reproducibility, and clinical study results hamper the translation of radiomics analysis into clinical practice.
INFECTIOUS AGENTS AND CANCER
(2023)
Review
Medicine, General & Internal
Francesca Grassi, Vincenza Granata, Roberta Fusco, Federica De Muzio, Carmen Cutolo, Michela Gabelloni, Alessandra Borgheresi, Ginevra Danti, Carmine Picone, Andrea Giovagnoni, Vittorio Miele, Nicoletta Gandolfo, Antonio Barile, Valerio Nardone, Roberta Grassi
Summary: The role of radiotherapy in the treatment of lung neoplasms, along with surgery and systemic therapies, has become essential. The focus has shifted towards improving survival outcomes, quality of life, treatment compliance, and management of side effects. Imaging plays a crucial role in evaluating treatment efficacy and identifying rare effects, especially when multiple treatments are involved. Radiation recall pneumonitis, a rare complication, needs to be recognized and characterized accurately, requiring prompt identification and the best therapeutic strategy for minimal disruption of ongoing cancer treatment. Artificial intelligence could play a critical role in this regard, provided a larger patient dataset is available.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Federica De Muzio, Roberta Fusco, Carmen Cutolo, Giuliana Giacobbe, Federico Bruno, Pierpaolo Palumbo, Ginevra Danti, Giulia Grazzini, Federica Flammia, Alessandra Borgheresi, Andrea Agostini, Francesca Grassi, Andrea Giovagnoni, Vittorio Miele, Antonio Barile, Vincenza Granata
Summary: Rectal cancer is a highly lethal malignancy and surgery is the most common treatment option. The choice of surgical approach aims to maximize function while minimizing the risk of recurrence, and is determined by a multidisciplinary team assessing patient and tumor characteristics. Total mesorectal excision, including both low anterior resection and abdominoperineal resection, remains the standard of care for rectal cancer. Rating: 8 out of 10.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Carmine Picone, Roberta Fusco, Michele Tonerini, Salvatore Claudio Fanni, Emanuele Neri, Maria Chiara Brunese, Roberta Grassi, Ginevra Danti, Antonella Petrillo, Mariano Scaglione, Nicoletta Gandolfo, Andrea Giovagnoni, Antonio Barile, Vittorio Miele, Claudio Granata, Vincenza Granata
Summary: In modern clinical practice, imaging techniques are increasingly used in emergencies, leading to a higher frequency of examinations and increased radiation exposure. The management of pregnant women is particularly critical as they are at higher risk. While ultrasound and magnetic resonance imaging are preferred, computed tomography remains necessary in certain cases. Protocol optimization is crucial in reducing risks. This review aims to evaluate different diagnostic tools and protocols to control radiation dose in emergency conditions involving abdominal pain and trauma.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Oncology
Umberto Committeri, Simona Barone, Giovanni Salzano, Antonio Arena, Gerardo Borriello, Francesco Giovacchini, Roberta Fusco, Luigi Angelo Vaira, Alfonso Scarpa, Vincenzo Abbate, Lorenzo Ugga, Pasquale Piombino, Franco Ionna, Luigi Califano, Giovanni Dell'Aversana Orabona
Summary: This study aimed to improve the effectiveness of pre-surgical diagnosis by using a machine learning tool to analyze inflammatory biomarkers and radiomic metrics extracted from MRI images to differentiate between benign and malignant salivary gland tumors. The results showed that both inflammatory biomarkers and radiomic features were capable of supporting a differential diagnosis between different types of tumors.
Review
Medicine, General & Internal
Orlando Catalano, Roberta Fusco, Federica De Muzio, Igino Simonetti, Pierpaolo Palumbo, Federico Bruno, Alessandra Borgheresi, Andrea Agostini, Michela Gabelloni, Carlo Varelli, Antonio Barile, Andrea Giovagnoni, Nicoletta Gandolfo, Vittorio Miele, Vincenza Granata
Summary: Breast ultrasound has made significant technological advancements, transitioning from a low-resolution grayscale technique to a high-performing, multiparametric modality. This review covers the range of commercially available technical tools, including microvasculature imaging, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced ultrasound, and more. It also discusses the expanded applications of breast ultrasound in clinical scenarios, such as primary ultrasound, complementary ultrasound, and second-look ultrasound. The review concludes by acknowledging the ongoing limitations and challenges in breast ultrasound.
Review
Radiology, Nuclear Medicine & Medical Imaging
Fabio Pellegrino, Vincenza Granata, Roberta Fusco, Francesca Grassi, Salvatore Tafuto, Luca Perrucci, Giulia Tralli, Mariano Scaglione
Summary: Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a heterogeneous group of tumors that arise from cells of the diffuse neuroendocrine system. They can be sporadic or occur in the context of genetic syndromes. They are mostly nonfunctioning, but some produce hormones responsible for clinical syndromes. The grade and differentiation of the tumor can affect clinical behaviors and prognoses. Diagnosis of GEP-NENs involves identifying the presence of the tumor and determining the primary site and extent of metastases. Morphological evaluations, such as CT and MRI, and functional evaluations, such as PET-CT and somatostatin analogs, are important in the diagnostic management of patients.
