研究者詳細

顔写真

レダ　ヘミ　スモア　サラ
Reda Helmy Sammour Sara
Reda Helmy Sammour Sara
所属
職名
助教
学位

  • 博士（歯学）（東北大学）

  • Ｍ．Ｓ．（Tanta university）

論文 6

  1. Impact of implant abutment connection designs and cyclic loading on screw stability in dental implants: A systematic review and meta-analysis

    Sara Reda Sammour, Xie Ziqi, Toru Ogawa, Nobuhiro Yoda

    JOURNAL OF PROSTHETIC DENTISTRY　135　(2)　2026年2月

    DOI： 10.1016/j.prosdent.2025.09.035 　

    ISSN：0022-3913

    eISSN：1097-6841

  2. Anomaly detection of screw loosening of different implant-abutment connection designs using resonance frequency analysis: An in vitro study. 国際誌

    Sara Reda Sammour, Hideki Naito, Ryuji Shigemitsu, Ziqi Xie, Tomoyuki Kimoto, Keiichi Sasaki, Nobuhiro Yoda, Toru Ogawa

    The Journal of prosthetic dentistry　2026年1月2日

    DOI： 10.1016/j.prosdent.2025.12.007 　

    詳細を見る 詳細を閉じる

    STATEMENT OF PROBLEM: Screw loosening is a common mechanical complication in implant-supported restorations, yet early detection remains challenging because of the lack of noninvasive, quantitative assessment tools. PURPOSE: This in vitro study aimed to evaluate the ability of resonance frequency analysis (RFA) combined with machine learning techniques to detect screw loosening in 3 implant-abutment connection designs: conical, internal tri-channel, and external hexagonal. MATERIAL AND METHODS: Three connection types were tested using 6 implants inserted into mandibular jaw models. RFA was conducted using a triaxial accelerometer under 7 loosening conditions with the frequency responses in the x, y, and z directions being recorded. Supervised learning (random forest) was used for binary (intact versus abnormal) and multiclass (7 classes) classification. Unsupervised learning was used to detect anomalies. RESULTS: The RFA revealed unique vibrational profiles for each connection design. The random forest classifier achieved 100% accuracy in binary classification and 83% to 92% in multiclass classification. Feature importance analysis highlighted the relevance of the triaxial vibration data. The convolutional autoencoder consistently identified screw loosening as early as 5 degrees. CONCLUSIONS: RFA combined with machine learning reliably detected screw loosening across various types of implant-abutment connections. This method offers a noninvasive, quantitative tool for the early detection of mechanical complications in implant dentistry.

  3. Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An <i>in vitro</i> study

    Sara Reda Sammour, Hideki Naito, Tomoyuki Kimoto, Keiichi Sasaki, Toru Ogawa

    Annals of Japan Prosthodontic Society　17　(4)　247-257　2025年

    出版者・発行元： Japan Prosthodontic Society

    DOI： 10.2186/ajps.17.247 　

    ISSN：1883-4426

    eISSN：1883-6860

  4. Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An in vitro study

    Sara Reda Sammour, Hideki Naito, Tomoyuki Kimoto, Keiichi Sasaki, Toru Ogawa

    JOURNAL OF PROSTHODONTIC RESEARCH　68　(4)　568-577　2024年

    DOI： 10.2186/jpr.JPR_D_23_00154 　

    ISSN：1883-1958

    eISSN：2212-4632

  5. Effectiveness of exercise therapy on pain relief and jaw mobility in patients with pain-related temporomandibular disorders: a systematic review

    Akiko Shimada, Toru Ogawa, Sara Reda Sammour, Taichi Narihara, Shiori Kinomura, Rie Koide, Noboru Noma, Keiichi Sasaki

    FRONTIERS IN ORAL HEALTH　4　2023年7月12日

    DOI： 10.3389/froh.2023.1170966 　

    eISSN：2673-4842

  6. Effect of implant abutment connection designs, and implant diameters on screw loosening before and after cyclic loading: In-vitro study

    Sara Reda Sammour, Mohamed Maamoun El-Sheikh, Attiah Aly El-Gendy

    DENTAL MATERIALS　35　(11)　E265-E271　2019年11月

    DOI： 10.1016/j.dental.2019.07.026 　

    ISSN：0109-5641

    eISSN：1879-0097

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