{"id":22623,"date":"2026-05-21T18:19:10","date_gmt":"2026-05-21T15:19:10","guid":{"rendered":"https:\/\/su.edu.ye\/voga\/?p=22623"},"modified":"2026-05-21T18:21:14","modified_gmt":"2026-05-21T15:21:14","slug":"masters-degree-awarded-to-ms-hiba-al-baadani-in-oral-and-maxillofacial-surgery","status":"publish","type":"post","link":"https:\/\/su.edu.ye\/voga\/en\/22623\/","title":{"rendered":"Master\u2019s Degree Awarded to Ms. Hiba Al-Ba\u2019adani \u200ein Oral and Maxillofacial Surgery"},"content":{"rendered":"

Ms. Hiba Abdo Mohammed Za\u2019atan Al-Ba\u2019adani <\/strong>was awarded a Master\u2019s Degree in Oral and Maxillofacial Surgery <\/strong>for her thesis titled: The Oral Biopsy versus Artificial Intelligence in Detection of Dental Cysts, Abscesses, and Tumors<\/em><\/strong>, <\/em>which was submitted to the Faculty of Dentistry \u200e \u2013 Sana\u2019a University. The MA defense was held on Saturday, March 14, 2026\u200e.<\/strong><\/p>\n

The MA Viva-voce Committee, which was formed based on a resolution issued by the Graduate Studies and Scientific Research Council, consisted of the following:<\/p>\n\n\n\n\n\n\n
#<\/strong><\/td>\nCommittee Members<\/strong><\/td>\nDesignation<\/strong><\/td>\nPosition<\/strong><\/td>\n<\/tr>\n
1<\/td>\nAssoc. Prof. Ahmed Saleh Yahya Al-Khatri \u200e<\/strong><\/td>\nExternal Examiner<\/td>\nChair<\/td>\n<\/tr>\n
2<\/td>\nAssoc. Prof. Sam Abdulkarim Mohammed Da\u2019er<\/strong><\/td>\nMain Supervisor<\/td>\nMember<\/td>\n<\/tr>\n
3<\/td>\nDr. Abdullah Farhan Al-Sharabi \u200e<\/strong><\/td>\nInternal Examiner<\/td>\nMember<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

The thesis aimed to:<\/p>\n

    \n
  • Develop and evaluate two separate deep learning models for the differential \u200ediagnosis of periapical lesions. \u200e<\/li>\n
  • Build a classification model using EfficientNetB3 architecture to categorize lesions \u200einto abscesses, cysts, and tumors. \u200e<\/li>\n
  • Develop a second model for lesion detection and segmentation using the YOLO \u200e \u200e<\/li>\n
  • Explore an assistive approach to reduce reliance on oral biopsy as an invasive and costly \u200eprocedure, by utilizing artificial intelligence for early and accurate diagnosis of oral \u200e<\/li>\n<\/ul>\n

    The study yielded several key findings summarized as follows:<\/p>\n

      \n
    • The classification model achieved a high accuracy of 96.40%, with good \u200eperformance in identifying abscesses (83.3%) and cysts (61.9%), but was unable \u200eto detect tumors due to limited available data. \u200e<\/li>\n
    • The detection model using YOLO achieved sensitivities of 65% for abscesses, \u200e\u200e5% for cysts, and 30% for tumors, highlighting the need for larger and more \u200ebalanced datasets to improve the performance of the models. \u200e<\/li>\n
    • The study confirmed that data size and balance are critical factors in the effectiveness \u200eof medical AI systems, particularly in rare tumor cases.\u200e<\/li>\n<\/ul>\n

      In light of these findings, the researcher recommended the following:\u200e<\/p>\n

        \n
      • Enhancing interactive content by developing more engaging and user-friendly tools, \u200esuch Developing larger and more balanced datasets, especially for tumor cases. \u200e<\/li>\n
      • Expanding the use of artificial intelligence applications in the diagnosis of oral and \u200edental diseases. \u200e<\/li>\n
      • Conducting further studies to improve model performance and enable clinical \u200e<\/li>\n
      • Enhancing interdisciplinary collaboration between medical and technical fields.\u200e<\/li>\n<\/ul>\n

        The defense session was attended by a number of academics, researchers, students, colleagues, and the researcher\u2019s family.<\/p>\n

        \"\" \"\" \"\"<\/p>\n