Giovanni Dimauro1, Giorgio Ciprandi2, Francesca Deperte1, Francesco Girardi3, Enrico Ladisa1, Sergio Latrofa1, Matteo Gelardi4. 1. Dipartimento di Informatica, Università degli Studi di Bari 'Aldo Moro', Bari, Italy. 2. Ospedale Policlinico San Martino, Genoa, Italy. Electronic address: gio.cip@libero.it. 3. UVARP ASL, Lungomare Starita, 4, 70123, Bari, Italy. 4. Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari 'Aldo Moro', Bari, Italy.
Abstract
BACKGROUND: In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driving changes in the previous classification of rhinitis. The simplicity of the technique and its low invasiveness make nasal cytology a practical diagnostic tool for all rhino-allergology services. Furthermore, since it allows the monitoring of responses to treatment, this method plays an important role in guiding a more effective and less expensive diagnostic program. Microscopic observation requires prolonged effort by a specialist, but the modern scanning systems for cytological preparations and the new affordable digital microscopes allow to design a software support system, based on deep learning techniques, to relieve specialist's tiring activity. BASIC PROCEDURE: By means of the system presented in this paper, it is possible to automatically identify and classify cells present on a nasal cytological preparation based on a digital image of the preparation itself. Thus, an interesting diagnostic support has been made available to the rhino-cytologist, who can quickly verify that the cells have been correctly classified by the software system: any few unclassified or incorrectly classified cells can be quickly sorted by the specialist itself, then one or more diagnosis can be suggested by this system, taking into consideration also the anamnesis of each patient. The final diagnosis can be defined by the specialist, also based on the result of the prick test and the observation of the nasal cavity. FINDINGS: In the system presented herein, image processing and image segmentation techniques have been used to find images of cellular elements within the preparation. Cell classification is based on a convolutional neural network composed of three blocks of main layers. Cell identification (first step, image segmentation) exhibits sensitivity greater than 97%, while cell classification (second step, seven cytotypes) attained a mean accuracy of approximately 99% on the test set and 94% on the validation set. CONCLUSIONS: This complete system supports clinicians in the preparation of a rhino-cytogram report.
BACKGROUND: In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driving changes in the previous classification of rhinitis. The simplicity of the technique and its low invasiveness make nasal cytology a practical diagnostic tool for all rhino-allergology services. Furthermore, since it allows the monitoring of responses to treatment, this method plays an important role in guiding a more effective and less expensive diagnostic program. Microscopic observation requires prolonged effort by a specialist, but the modern scanning systems for cytological preparations and the new affordable digital microscopes allow to design a software support system, based on deep learning techniques, to relieve specialist's tiring activity. BASIC PROCEDURE: By means of the system presented in this paper, it is possible to automatically identify and classify cells present on a nasal cytological preparation based on a digital image of the preparation itself. Thus, an interesting diagnostic support has been made available to the rhino-cytologist, who can quickly verify that the cells have been correctly classified by the software system: any few unclassified or incorrectly classified cells can be quickly sorted by the specialist itself, then one or more diagnosis can be suggested by this system, taking into consideration also the anamnesis of each patient. The final diagnosis can be defined by the specialist, also based on the result of the prick test and the observation of the nasal cavity. FINDINGS: In the system presented herein, image processing and image segmentation techniques have been used to find images of cellular elements within the preparation. Cell classification is based on a convolutional neural network composed of three blocks of main layers. Cell identification (first step, image segmentation) exhibits sensitivity greater than 97%, while cell classification (second step, seven cytotypes) attained a mean accuracy of approximately 99% on the test set and 94% on the validation set. CONCLUSIONS: This complete system supports clinicians in the preparation of a rhino-cytogram report.
Authors: Antonio Mario Bulfamante; Francesco Ferella; Austin Michael Miller; Cecilia Rosso; Carlotta Pipolo; Emanuela Fuccillo; Giovanni Felisati; Alberto Maria Saibene Journal: Eur Arch Otorhinolaryngol Date: 2022-10-19 Impact factor: 3.236