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Gagna CE, Yodice AN, D'Amico J, Elkoulily L, Gill SM, DeOcampo FG, Rabbani M, Kaur J, Shah A, Ahmad Z, Lambert MW, Clark Lambert W. Novel B-DNA dermatophyte assay for demonstration of canonical DNA in dermatophytes: Histopathologic characterization by artificial intelligence. Clin Dermatol 2024; 42:233-258. [PMID: 38185195 DOI: 10.1016/j.clindermatol.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
We describe a novel assay and artificial intelligence-driven histopathologic approach identifying dermatophytes in human skin tissue sections (ie, B-DNA dermatophyte assay) and demonstrate, for the first time, the presence of dermatophytes in tissue using immunohistochemistry to detect canonical right-handed double-stranded (ds) B-DNA. Immunohistochemistry was performed using anti-ds-B-DNA monoclonal antibodies with formalin-fixed paraffin-embedded tissues to determine the presence of dermatophytes. The B-DNA assay resulted in a more accurate identification of dermatophytes, nuclear morphology, dimensions, and gene expression of dermatophytes (ie, optical density values) than periodic acid-Schiff (PAS), Grocott methenamine silver (GMS), or hematoxylin and eosin (H&E) stains. The novel assay guided by artificial intelligence allowed for efficient identification of different types of dermatophytes (eg, hyphae, microconidia, macroconidia, and arthroconidia). Using the B-DNA dermatophyte assay as a clinical tool for diagnosing dermatophytes is an alternative to PAS, GMS, and H&E as a fast and inexpensive way to accurately detect dermatophytosis and reduce the number of false negatives. Our assay resulted in superior identification, sensitivity, life cycle stages, and morphology compared to H&E, PAS, and GMS stains. This method detects a specific structural marker (ie, ds-B-DNA), which can assist with diagnosis of dermatophytes. It represents a significant advantage over methods currently in use.
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Affiliation(s)
- Claude E Gagna
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA; Department of Pathology and Laboratory Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA; Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA; Department of Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA.
| | - Anthony N Yodice
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Juliana D'Amico
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Lina Elkoulily
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Shaheryar M Gill
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Francis G DeOcampo
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Maryam Rabbani
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Jai Kaur
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Aangi Shah
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Zainab Ahmad
- Department of Biological and Chemical Sciences, College of Arts and Sciences, New York Institute of Technology, Old Westbury, New York, USA
| | - Muriel W Lambert
- Department of Pathology and Laboratory Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA; Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - W Clark Lambert
- Department of Pathology and Laboratory Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA; Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA; Department of Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
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Marozzi MS, Cicco S, Mancini F, Corvasce F, Lombardi FA, Desantis V, Loponte L, Giliberti T, Morelli CM, Longo S, Lauletta G, Solimando AG, Ria R, Vacca A. A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study. Diagnostics (Basel) 2024; 14:155. [PMID: 38248032 PMCID: PMC10814651 DOI: 10.3390/diagnostics14020155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/06/2024] [Accepted: 01/07/2024] [Indexed: 01/23/2024] Open
Abstract
INTRODUCTION Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency. AIM This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The "SensUS Lung" device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement. METHODS We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm ("SensUS Lung") using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann-Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant. RESULTS Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0-2] vs. LUS 0.67 [0.25-1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = -0.40, p = 0.027), forced vital capacity (FVC%, r = -0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = -0.39, p = 0.02) while results directly correlated with FEF25-75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1-2] vs. preserved 0 [0-1], p = 0.001), and overlapping the LUS (reduced median 18 [4-20] vs. preserved 5.5 [2-9], p = 0.035). CONCLUSIONS Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills.
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Affiliation(s)
- Marialuisa Sveva Marozzi
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Sebastiano Cicco
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Francesca Mancini
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Francesco Corvasce
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | | | - Vanessa Desantis
- Pharmacology Section, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Luciana Loponte
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Tiziana Giliberti
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Claudia Maria Morelli
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Stefania Longo
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Gianfranco Lauletta
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Antonio G. Solimando
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Roberto Ria
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Angelo Vacca
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
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Mosquera-Zamudio A, Launet L, Del Amor R, Moscardó A, Colomer A, Naranjo V, Monteagudo C. A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models. Sci Data 2023; 10:704. [PMID: 37845235 PMCID: PMC10579378 DOI: 10.1038/s41597-023-02585-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 09/22/2023] [Indexed: 10/18/2023] Open
Abstract
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of unknown malignant potential (STUMP) or atypical Spitz tumor. Studies developing deep learning (DL) models to diagnose melanocytic tumors using whole slide imaging (WSI) are scarce, and few used ST for analysis, excluding STUMP. To address this gap, we introduce SOPHIE: the first ST dataset with WSIs, including labels as benign, malignant, and atypical tumors, along with the clinical information of each patient. Additionally, we explain two DL models implemented as validation examples using this database.
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Affiliation(s)
- Andrés Mosquera-Zamudio
- Pathology Department Hospital Clínico Universitario de Valencia, Universidad de Valencia, Valencia, Spain.
- INCLIVA, Instituto de Investigación Sanitaria, Valencia, Spain.
| | - Laëtitia Launet
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de València, Valencia, Spain
| | - Rocío Del Amor
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de València, Valencia, Spain
| | - Anaïs Moscardó
- Pathology Department Hospital Clínico Universitario de Valencia, Universidad de Valencia, Valencia, Spain
| | - Adrián Colomer
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de València, Valencia, Spain
- valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | - Valery Naranjo
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de València, Valencia, Spain
- valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | - Carlos Monteagudo
- Pathology Department Hospital Clínico Universitario de Valencia, Universidad de Valencia, Valencia, Spain.
- INCLIVA, Instituto de Investigación Sanitaria, Valencia, Spain.
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Sauter D, Lodde G, Nensa F, Schadendorf D, Livingstone E, Kukuk M. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review. Comput Biol Med 2023; 163:107083. [PMID: 37315382 DOI: 10.1016/j.compbiomed.2023.107083] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/10/2023] [Accepted: 05/27/2023] [Indexed: 06/16/2023]
Abstract
Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the current literature. We aim to provide a structured and comprehensive overview of peer-reviewed publications on DL applied to dermatopathology focused on melanoma. In comparison to well-published DL methods on non-medical images (e.g., classification on ImageNet), this field of application comprises a specific set of challenges, such as staining artifacts, large gigapixel images, and various magnification levels. Thus, we are particularly interested in the pathology-specific technical state-of-the-art. We also aim to summarize the best performances achieved thus far with respect to accuracy, along with an overview of self-reported limitations. Accordingly, we conducted a systematic literature review of peer-reviewed journal and conference articles published between 2012 and 2022 in the databases ACM Digital Library, Embase, IEEE Xplore, PubMed, and Scopus, expanded by forward and backward searches to identify 495 potentially eligible studies. After screening for relevance and quality, a total of 54 studies were included. We qualitatively summarized and analyzed these studies from technical, problem-oriented, and task-oriented perspectives. Our findings suggest that the technical aspects of DL for histopathology in melanoma can be further improved. The DL methodology was adopted later in this field, and still lacks the wider adoption of DL methods already shown to be effective for other applications. We also discuss upcoming trends toward ImageNet-based feature extraction and larger models. While DL has achieved human-competitive accuracy in routine pathological tasks, its performance on advanced tasks is still inferior to wet-lab testing (for example). Finally, we discuss the challenges impeding the translation of DL methods to clinical practice and provide insight into future research directions.
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Affiliation(s)
- Daniel Sauter
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany.
| | - Georg Lodde
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | - Felix Nensa
- Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | | | - Markus Kukuk
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany
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Herzum A, Occella C, Vellone VG, Gariazzo L, Pastorino C, Ferro J, Sementa A, Mazzocco K, Vercellino N, Viglizzo G. Paediatric Spitzoid Neoplasms: 10-Year Retrospective Study Characterizing Histological, Clinical, Dermoscopic Presentation and FISH Test Results. Diagnostics (Basel) 2023; 13:2380. [PMID: 37510125 PMCID: PMC10378405 DOI: 10.3390/diagnostics13142380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION Spitzoid lesions are a wide tumour class comprising Spitz nevus (SN), atypical Spitz tumour (AST) and Spitz melanoma (SM). MATERIALS AND METHODS We conducted a single-centre-based retrospective survey on all histologically diagnosed spitzoid lesions of paediatric patients (1-18 years) of the last 10 years (2012-2022). Histopathological reports and electronic records of patients were used to retrieve relevant data regarding patients' features, clinical and dermatoscopical aspects of lesions when recorded, and FISH tests when present. RESULTS Of 255 lesions, 82% were histologically benign, 17% atypical, 1% malignant. Clinically, 100% of SM were large (≥6 mm) and raised; AST were mainly large (63%), raised (98%), pink (95%). Small (≤5 mm), pigmented, flat lesions correlated with benign histology (respectively 90%, 97%, 98% SN) (p < 0.0001). Dermatoscopical patterns were analysed in 100 patients: starburst pattern correlated with benign histology (26% SN (p = 0.004)), while multicomponent pattern correlated with atypical/malignant lesions (56% AST, 50% SM (p = 0.0052)). Eighty-five lesions were subjected to fluorescence in situ hybridization (FISH): 34 (71% AST; 29% SN) were FISH-positive; 51 (63% SN; 37% AST) were FISH-negative (p = 0.0038). DISCUSSION This study confirmed predominant benign histology (82%) of paediatric spitzoid lesions, thus detecting 17% AST and 1% SM, highlighting the need for caution in handling spitzoid lesions. CONCLUSION Until AST are considered potentially malignant proliferations and no reliable criteria are identified to distinguish them, the authors suggest a prudent approach, especially in children.
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Affiliation(s)
- Astrid Herzum
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Corrado Occella
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Valerio Gaetano Vellone
- Pathology Unit, U.O.C. Anatomia Patologica, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Lodovica Gariazzo
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Carlotta Pastorino
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Jacopo Ferro
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Angela Sementa
- Pathology Unit, U.O.C. Anatomia Patologica, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Katia Mazzocco
- Pathology Unit, U.O.C. Anatomia Patologica, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Nadia Vercellino
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
| | - Gianmaria Viglizzo
- Dermatology Unit, U.O.C. Dermatologia e Centro Angiomi, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, 5-16147 Genova, Italy
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Cazzato G, Massaro A, Colagrande A, Trilli I, Ingravallo G, Casatta N, Lupo C, Ronchi A, Franco R, Maiorano E, Vacca A. Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology. Curr Oncol 2023; 30:6066-6078. [PMID: 37504312 PMCID: PMC10378276 DOI: 10.3390/curroncol30070452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 07/29/2023] Open
Abstract
Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.
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Affiliation(s)
- Gerardo Cazzato
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Alessandro Massaro
- LUM Enterprise srl, S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy
- Department of Management, Finance and Technology, LUM-Libera Università Mediterranea "Giuseppe Degennaro", S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy
| | - Anna Colagrande
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Irma Trilli
- Odontomatostologic Clinic, Department of Innovative Technologies in Medicine and Dentistry, University of Chieti "G. D'Annunzio", 66100 Chieti, Italy
| | - Giuseppe Ingravallo
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Nadia Casatta
- Innovation Department, Diapath S.p.A., Via Savoldini n.71, 24057 Martinengo, Italy
| | - Carmelo Lupo
- Innovation Department, Diapath S.p.A., Via Savoldini n.71, 24057 Martinengo, Italy
| | - Andrea Ronchi
- Pathology Unit, Department of Mental Health and Physic and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Renato Franco
- Pathology Unit, Department of Mental Health and Physic and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Eugenio Maiorano
- Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
| | - Angelo Vacca
- Centro Interdisciplinare Ricerca Telemedicina-CITEL, Università degli Studi di Bari "Aldo Moro", 70124 Bari, Italy
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Doeleman T, Hondelink LM, Vermeer MH, van Dijk MR, Schrader AMR. Artificial intelligence in digital pathology of cutaneous lymphomas: a review of the current state and future perspectives. Semin Cancer Biol 2023:S1044-579X(23)00095-0. [PMID: 37331571 DOI: 10.1016/j.semcancer.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/20/2023]
Abstract
Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients' whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL.
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Affiliation(s)
- Thom Doeleman
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Liesbeth M Hondelink
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Maarten H Vermeer
- Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Marijke R van Dijk
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anne M R Schrader
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands
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Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review. Cancers (Basel) 2022; 15:cancers15010042. [PMID: 36612037 PMCID: PMC9817526 DOI: 10.3390/cancers15010042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis. Embase, Pubmed, Web of Science, and Virtual Health Library were used to search for relevant studies for the systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Articles from 2015 to July 2022 were included, with an emphasis placed on the used artificial intelligence methods. Twenty-eight studies that fulfilled the inclusion criteria were grouped into four groups based on their clinical objectives, including pathologists versus deep learning models (n = 10), diagnostic prediction (n = 7); prognosis (n = 5), and histological features (n = 6). These were then analyzed to draw conclusions on the general parameters and conditions of AI in pathology, as well as the necessary factors for better performance in real scenarios.
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Arezzo F, Cormio G, La Forgia D, Santarsiero CM, Mongelli M, Lombardi C, Cazzato G, Cicinelli E, Loizzi V. A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients. Arch Gynecol Obstet 2022; 306:2143-2154. [PMID: 35532797 PMCID: PMC9633520 DOI: 10.1007/s00404-022-06578-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/12/2022] [Indexed: 02/05/2023]
Abstract
In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS.
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Affiliation(s)
- Francesca Arezzo
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Gennaro Cormio
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Daniele La Forgia
- Department of Breast Radiology, Giovanni Paolo II I.R.C.C.S. Cancer Institute, via Orazio Flacco 65, 70124 Bari, Italy
| | - Carla Mariaflavia Santarsiero
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Michele Mongelli
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Claudio Lombardi
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Gerardo Cazzato
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Ettore Cicinelli
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Vera Loizzi
- Interdisciplinar Department of Medicine, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
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Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy. Arch Gynecol Obstet 2022; 307:1911-1919. [PMID: 36370209 DOI: 10.1007/s00404-022-06824-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore, the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show conflicting data. Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients. METHODS We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with tenfold cross-validation. The performances of the algorithm were assessed. RESULTS Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pre-treatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre- and post-treatment MRI, presence/absence of fornix infiltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79). CONCLUSIONS We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.
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Aldhyani THH, Verma A, Al-Adhaileh MH, Koundal D. Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12092048. [PMID: 36140447 PMCID: PMC9497471 DOI: 10.3390/diagnostics12092048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Skin is the primary protective layer of the internal organs of the body. Nowadays, due to increasing pollution and multiple other factors, various types of skin diseases are growing globally. With variable shapes and multiple types, the classification of skin lesions is a challenging task. Motivated by this spreading deformity in society, a lightweight and efficient model is proposed for the highly accurate classification of skin lesions. Dynamic-sized kernels are used in layers to obtain the best results, resulting in very few trainable parameters. Further, both ReLU and leakyReLU activation functions are purposefully used in the proposed model. The model accurately classified all of the classes of the HAM10000 dataset. The model achieved an overall accuracy of 97.85%, which is much better than multiple state-of-the-art heavy models. Further, our work is compared with some popular state-of-the-art and recent existing models.
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Affiliation(s)
- Theyazn H. H. Aldhyani
- Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
- Correspondence:
| | - Amit Verma
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
| | - Mosleh Hmoud Al-Adhaileh
- Deanship of E-Learning and Distance Education, King Faisal University, P.O. Box 4000, Al-Ahsa 31982, Saudi Arabia
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, India
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Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation. Diagnostics (Basel) 2022; 12:diagnostics12071713. [PMID: 35885617 PMCID: PMC9316584 DOI: 10.3390/diagnostics12071713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 11/16/2022] Open
Abstract
Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.
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An Audit and Survey of Informal Use of Instant Messaging for Dermatology in District Hospitals in KwaZulu-Natal, South Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127462. [PMID: 35742708 PMCID: PMC9223770 DOI: 10.3390/ijerph19127462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 02/04/2023]
Abstract
Background. In KwaZulu-Natal (KZ-N), South Africa, recent reports have indicated that spontaneous use of smartphones has occurred, providing access to specialist dermatological care to remote areas. This informal use has raised a number of practical, legal, regulatory, and ethical concerns. Aim. To assess the nature and content of WhatsApp messages sent to dermatologists, to determine the referring doctors’ reasons for, and satisfaction with, their interactions, as well as their knowledge of legal, regulatory, and ethical requirements. Methods. A retrospective study of WhatsApp messages between referring doctors and dermatologists, as well as a cross-sectional survey of doctors working at district hospitals in KZ-N who used IM for teledermatology. Results. Use of IM (primarily WhatsApp) for teledermatology was almost universal, but often not considered ‘telemedicine’. Few referring doctors were aware of South Africa’s ethical guidelines and their requirements, and few of those who did followed them, e.g., the stipulated and onerous consent process and existing privacy and security legislations. No secure methods for record keeping or data storage of WhatsApp content were used. A desire to formalize the service existed. Conclusions. Based upon these findings, it was proposed that a number of described steps be followed in order to formalize the use of IM for teledermatology.
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Venerito V, Emmi G, Cantarini L, Leccese P, Fornaro M, Fabiani C, Lascaro N, Coladonato L, Mattioli I, Righetti G, Malandrino D, Tangaro S, Palermo A, Urban ML, Conticini E, Frediani B, Iannone F, Lopalco G. Validity of Machine Learning in Predicting Giant Cell Arteritis Flare After Glucocorticoids Tapering. Front Immunol 2022; 13:860877. [PMID: 35450069 PMCID: PMC9017227 DOI: 10.3389/fimmu.2022.860877] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background Inferential statistical methods failed in identifying reliable biomarkers and risk factors for relapsing giant cell arteritis (GCA) after glucocorticoids (GCs) tapering. A ML approach allows to handle complex non-linear relationships between patient attributes that are hard to model with traditional statistical methods, merging them to output a forecast or a probability for a given outcome. Objective The objective of the study was to assess whether ML algorithms can predict GCA relapse after GCs tapering. Methods GCA patients who underwent GCs therapy and regular follow-up visits for at least 12 months, were retrospectively analyzed and used for implementing 3 ML algorithms, namely, Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The outcome of interest was disease relapse within 3 months during GCs tapering. After a ML variable selection method, based on a XGBoost wrapper, an attribute core set was used to train and test each algorithm using 5-fold cross-validation. The performance of each algorithm in both phases was assessed in terms of accuracy and area under receiver operating characteristic curve (AUROC). Results The dataset consisted of 107 GCA patients (73 women, 68.2%) with mean age ( ± SD) 74.1 ( ± 8.5) years at presentation. GCA flare occurred in 40/107 patients (37.4%) within 3 months after GCs tapering. As a result of ML wrapper, the attribute core set with the least number of variables used for algorithm training included presence/absence of diabetes mellitus and concomitant polymyalgia rheumatica as well as erythrocyte sedimentation rate level at GCs baseline. RF showed the best performance, being significantly superior to other algorithms in accuracy (RF 71.4% vs LR 70.4% vs DT 62.9%). Consistently, RF precision (72.1%) was significantly greater than those of LR (62.6%) and DT (50.8%). Conversely, LR was superior to RF and DT in recall (RF 60% vs LR 62.5% vs DT 47.5%). Moreover, RF AUROC (0.76) was more significant compared to LR (0.73) and DT (0.65). Conclusions RF algorithm can predict GCA relapse after GCs tapering with sufficient accuracy. To date, this is one of the most accurate predictive modelings for such outcome. This ML method represents a reproducible tool, capable of supporting clinicians in GCA patient management.
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Affiliation(s)
- Vincenzo Venerito
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Giacomo Emmi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Luca Cantarini
- Research Centre of Systemic Autoinflammatory Diseases, Behçet's Disease Clinic and Rheumatology-Ophthalmology Collaborative Uveitis Centre, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Pietro Leccese
- Rheumatology Department of Lucania, San Carlo Hospital of Potenza, Potenza, Italy
| | - Marco Fornaro
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Claudia Fabiani
- Ophthalmology Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nancy Lascaro
- Rheumatology Department of Lucania, San Carlo Hospital of Potenza, Potenza, Italy
| | - Laura Coladonato
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Irene Mattioli
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Giulia Righetti
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Danilo Malandrino
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, University of Bari "Aldo Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, Italy
| | - Adalgisa Palermo
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Maria Letizia Urban
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Edoardo Conticini
- Research Centre of Systemic Autoinflammatory Diseases, Behçet's Disease Clinic and Rheumatology-Ophthalmology Collaborative Uveitis Centre, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Bruno Frediani
- Research Centre of Systemic Autoinflammatory Diseases, Behçet's Disease Clinic and Rheumatology-Ophthalmology Collaborative Uveitis Centre, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Florenzo Iannone
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Giuseppe Lopalco
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
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Chen SB, Novoa RA. Artificial intelligence for dermatopathology: Current trends and the road ahead. Semin Diagn Pathol 2022; 39:298-304. [DOI: 10.1053/j.semdp.2022.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/06/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023]
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Thapar P, Rakhra M, Cazzato G, Hossain MS. A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1709842. [PMID: 35480147 PMCID: PMC9038388 DOI: 10.1155/2022/1709842] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/12/2022] [Accepted: 04/01/2022] [Indexed: 02/08/2023]
Abstract
Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.
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Affiliation(s)
- Puneet Thapar
- 1Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Manik Rakhra
- 1Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Gerardo Cazzato
- 2Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, Bari BA, Italy
| | - Md Shamim Hossain
- 3Department of Marketing, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
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