{
    "article_id": 1429,
    "public_id": "a3007b3a-deca-4c1f-8e11-37f2ee4059cb",
    "internal_identifier": null,
    "source_identifier": "88:14",
    "status": "published",
    "type": "research_article",
    "title": "3D Ağız İçi Taramalarına ait  Nokta Bulutu Verileri ile Derin Öğrenme Kullanılarak Diş Tipi Sınıflandırması",
    "title_secondary": null,
    "abstract": "Son yıllarda üretim teknolojilerindeki gelişmeler, daha düşük maliyetli gelişmiş ağız içi tarayıcıların ortaya çıkmasını ve kısa sürede diş hekimliğinde kullanılmasına neden olmuştur Literatüre bakıldığında ağız içi taramalarından elde edilen ham verinin doğrudan kullanımına yönelik veya farklı 3 boyutlu veri temsil modellerine dönüştürülerek bireysel diş tanımlama, diş damak ayrımı, diş segmentasyonu ve dişlerin sınıflandırılması konusunda çalışmaların yapıldığı görülmektedir. Bu çalışmada ağız içi tarayıcılar ile elde edilen örgü (mesh) verileri 3 boyutlu nokta bulutu ağız içi diş modellerine dönüştürülmüştür. Bu 3 boyutlu diş modelleri PointNet modeli kullanılarak sınıflandırılmıştır. Çalışmada nokta sayıları kademeli olarak artırılarak ve farklı optimizasyon fonksiyonlarına göre sınıflandırma modelinin performans metrikleri incelenmiştir. Yapılan denemeler ve elde edilen sınıflandırma metrikleri açısından incelendiğinde, çalışma için en iyi sonucun 2048 nokta sayısında Doğruluk %55, Kesinlik %55, Duyarlılık %55 ve F1 skoru %54 olarak bulunmuştur. Ayrıca Adam fonksiyonu, farklı optimizasyon fonksiyonları arasında en iyi performansa sahip optimizasyon fonksiyonu olduğu anlaşılmıştır.",
    "language": "tr",
    "english_title": "Tooth Type Classification Using Deep Learning with Point Cloud on 3D Intraoral Scanners",
    "english_abstract": "In recent years, developments in production technologies have led to the emergence of lower cost advanced intraoral scanners and their use in dentistry in a short time. When the literature is examined, it is seen that the raw data obtained from intraoral scans are converted into different 3D data representation models for direct use or by converting them into individual tooth identification, tooth-giginva separation, tooth segmentation and tooth classification. In this study, the mesh data obtained with intraoral scanners were converted into 3D point cloud intraoral tooth models. These 3D tooth models were classified using the PointNet model. In the study, the performance analyzes of the classification model were examined by increasing the sample of points gradually. When the results were analyzed in terms of trials and the classification metrics obtained, the best result for the study was found to be Accuracy 55%, Precision 55%, Recall 55%, and F1 score 54% in sample 2048 points. Also, Adam was the optimization function with the best performance among the different optimization functions.",
    "doi": "10.36287/setsci.5.1.014",
    "doi_url": "https://doi.org/10.36287/setsci.5.1.014",
    "resource_url": "https://www.set-science.com/a/a3007b3a-deca-4c1f-8e11-37f2ee4059cb",
    "pdf_url": "https://www.set-science.com/a/a3007b3a-deca-4c1f-8e11-37f2ee4059cb/pdf",
    "og_image": "https://www.set-science.com/assets/img/og-card-v2.png",
    "authors": [
        {
            "public_id": "ea9d242b-de03-4691-9a81-b91264c3b1f3",
            "given": "Furkan",
            "family": "Şenyurt",
            "full": "Furkan Şenyurt",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "furkan.senyurt@bil.omu.edu.tr",
            "corresponding": false,
            "sequence": "first",
            "affiliations": [
                {
                    "name": "Ondokuz Mayıs Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        },
        {
            "public_id": "3a0f9539-82c0-400e-b9b6-866f48538b40",
            "given": "İsmail",
            "family": "İşeri",
            "full": "İsmail İşeri",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "ismail.iseri@omu.edu.tr",
            "corresponding": true,
            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Ondokuz Mayıs Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        }
    ],
    "keywords": [
        "3B Derin Öğrenme",
        "3B Ağız-İçi Tarayıcıları",
        "Diş Sınıflandırma",
        "3B Dental Model",
        "Nokta Bulutu"
    ],
    "translations": [
        {
            "language": "en",
            "title": "Tooth Type Classification Using Deep Learning with Point Cloud on 3D Intraoral Scanners",
            "abstract": "In recent years, developments in production technologies have led to the emergence of lower cost advanced intraoral scanners and their use in dentistry in a short time. When the literature is examined, it is seen that the raw data obtained from intraoral scans are converted into different 3D data representation models for direct use or by converting them into individual tooth identification, tooth-giginva separation, tooth segmentation and tooth classification. In this study, the mesh data obtained with intraoral scanners were converted into 3D point cloud intraoral tooth models. These 3D tooth models were classified using the PointNet model. In the study, the performance analyzes of the classification model were examined by increasing the sample of points gradually. When the results were analyzed in terms of trials and the classification metrics obtained, the best result for the study was found to be Accuracy 55%, Precision 55%, Recall 55%, and F1 score 54% in sample 2048 points. Also, Adam was the optimization function with the best performance among the different optimization functions.",
            "keywords": [
                "3D Deep Learning",
                "3D Intraoral Scanners",
                "Tooth Classification",
                "3D Dental Model",
                "Point Cloud"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2022-06-01",
    "published_year": "2022",
    "received_date": null,
    "accepted_date": null,
    "volume": "13",
    "issue": null,
    "page_first": "69",
    "page_last": "74",
    "article_number": null,
    "conference": {
        "short_name": "ISAS2022",
        "full_title": "5th International Symposium on Innovative Approaches in Smart Technologies",
        "name": "5th International Symposium on Innovative Approaches in Smart Technologies",
        "acronym": "ISAS2022",
        "location": "Online, Türkiye",
        "start_date": "2022-05-28",
        "end_date": null,
        "public_id": "23436f3c-bde1-4f59-b3be-a8ccc770539f"
    },
    "proceedings": {
        "title": "Proceeding of the 5th International Symposium on Innovative Approaches in Smart Technologies",
        "series_title": "SETSCI Conference Proceedings",
        "publisher": "SET Technology",
        "isbn": null,
        "eisbn": "978-625-95366-9-9",
        "issn": null,
        "eissn": "2687-5527",
        "doi": null,
        "doi_prefix": "10.36287",
        "slug": "setsci-volume-13-2022-legacy-88",
        "public_id": "04c79b09-4b3a-4deb-84ea-b349d80e3e9e",
        "url": "https://www.set-science.com/p/setsci-volume-13-2022-legacy-88"
    },
    "license": {
        "name": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "code": "CC-BY-4.0",
        "open_access": true
    },
    "copyright": {
        "holder": "SET Technology",
        "year": "2022"
    },
    "funding_statement": null,
    "funders": [],
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    "history": {
        "published": "2022-06-01"
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}