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Cascarano GD, Debitonto FS, Lemma R, Brunetti A, Buongiorno D, De Feudis I, Guerriero A, Venere U, Matino S, Rocchetti MT, Rossini M, Pesce F, Gesualdo L, Bevilacqua V. A neural network for glomerulus classification based on histological images of kidney biopsy. BMC Med Inform Decis Mak 2021; 21:300. [PMID: 34724926 PMCID: PMC8559346 DOI: 10.1186/s12911-021-01650-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/06/2021] [Indexed: 11/26/2022] Open
Abstract
Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results.
CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes.
We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.
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Affiliation(s)
- Giacomo Donato Cascarano
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | | | - Ruggero Lemma
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | - Irio De Feudis
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | - Andrea Guerriero
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy
| | - Umberto Venere
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Silvia Matino
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Maria Teresa Rocchetti
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Michele Rossini
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Francesco Pesce
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Loreto Gesualdo
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy. .,Apulian Bioengineering s.r.l., Modugno, BA, Italy.
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Nannavecchia A, Girardi F, Fina PR, Scalera M, Dimauro G. Personal Heart Health Monitoring Based on 1D Convolutional Neural Network. J Imaging 2021; 7:26. [PMID: 34460625 PMCID: PMC8321282 DOI: 10.3390/jimaging7020026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/05/2022] Open
Abstract
The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.
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Affiliation(s)
- Antonella Nannavecchia
- Department of Management, Finance and Technology, University LUM Jean Monnet, 70010 Casamassima, Italy;
| | | | - Pio Raffaele Fina
- Department of Computer Science, University of Torino, 10124 Torino, Italy;
| | - Michele Scalera
- Department of Computer Science, University of Bari, 70125 Bari, Italy;
| | - Giovanni Dimauro
- Department of Computer Science, University of Bari, 70125 Bari, Italy;
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Abstract
Rhinology studies the anatomy, physiology, and diseases affecting the nasal region—one of the most modern techniques to diagnose these diseases is nasal cytology, which involves microscopic analysis of the cells contained in the nasal mucosa. The standard clinical protocol regulates the compilation of the rhino-cytogram by observing, for each slide, at least 50 fields under an optical microscope to evaluate the cell population and search for cells important for diagnosis. The time and effort required for the specialist to analyze a slide are significant. In this paper, we present a smartphones-based system to support cell segmentation on images acquired directly from the microscope. Then, the specialist can analyze the cells and the other elements extracted directly or, alternatively, he can send them to Rhino-cyt, a server system recently presented in the literature, that also performs the automatic cell classification, giving back the final rhinocytogram. This way he significantly reduces the time for diagnosing. The system crops cells with sensitivity = 0.96, which is satisfactory because it shows that cells are not overlooked as false negatives are few, and therefore largely sufficient to support the specialist effectively. The use of traditional image processing techniques to preprocess the images also makes the process sustainable from the computational point of view for medium–low end architectures and is battery-efficient on a mobile phone.
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