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    AI-powered segmentation of bifid mandibular canals using CBCT
    (Bmc, 2025) Gumussoy, Ismail; Demirezer, Kardelen; Duman, Suayip Burak; Haylaz, Emre; Bayrakdar, Ibrahim Sevki; Celik, Ozer; Syed, Ali Zakir
    ObjectiveAccurate segmentation of the mandibular and bifid canals is crucial in dental implant planning to ensure safe implant placement, third molar extractions and other surgical interventions. The objective of this study is to develop and validate an innovative artificial intelligence tool for the efficient, and accurate segmentation of the mandibular and bifid canals on CBCT.Materials and methodsCBCT data were screened to identify patients with clearly visible bifid canal variations, and their DICOM files were extracted. These DICOM files were then imported into the 3D Slicer (R) open-source software, where bifid canals and mandibular canals were annotated. The annotated data, along with the raw DICOM files, were processed using the nnU-Netv2 training model by CranioCatch AI software team.Results69 anonymized CBCT volumes in DICOM format were converted to NIfTI file format. The method, utilizing nnU-Net v2, accurately predicted the voxels associated with the mandibular canal, achieving an intersection of over 50% in nearly all samples. The accuracy, Dice score, precision, and recall scores for the mandibular canal/bifid canal were determined to be 0.99/0.99, 0.82/0.46, 0.85/0.70, and 0.80/0.42, respectively.ConclusionsDespite the bifid canal segmentation not meeting the expected level of success, the findings indicate that the proposed method shows promising and has the potential to be utilized as a supplementary tool for mandibular canal segmentation. Due to the significance of accurately evaluating the mandibular canal before surgery, the use of artificial intelligence could assist in reducing the burden on practitioners by automating the complicated and time-consuming process of tracing and segmenting this structure.Clinical relevanceBeing able to distinguish bifid channels with artificial intelligence will help prevent neurovascular problems that may occur before or after surgery.
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    Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images
    (Wiley-Hindawi, 2023) Syed, Ali Zakir; Ozen, Duygu Celik; Abdelkarim, Ahmed Z.; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Duman, Sacide; Celik, Ozer
    The detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients' cone-beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model's robustness. The proposed model showed promising potential for the automated detection of mesiodens, providing valuable assistance to dentists in accurate diagnosis.
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    Clinical and radiographic outcomes of four pulpotomy agents in primary molars: a prospective randomized controlled trial
    (Springer, 2025) Vural, Handan; Senem Ozsunkar, Pelin; Duman, Sacide; Syed, Ali Zakir; Tirasci, Gizem
    This split-mouth randomized controlled clinical trial aimed to assess the 12-month clinical and radiographic outcomes of four different pulpotomy materials in primary molars. The materials evaluated were Mineral Trioxide Aggregate (MTA), Biodentine, Ferric Sulfate (FS), and Sodium Hypochlorite (NaOCl) gel. Healthy children aged 4-7 with four primary molars requiring pulpotomy were included. Coronal pulpotomy was performed, followed by application of one of the four materials, and restoration with stainless steel crowns. Clinical and radiographic assessments were conducted at 6 and 12 months. Success rates were compared using Fisher's Exact and Cochran's Q tests. A total of 22 children (88 teeth) completed the study. At 12 months, clinical success was 100% in the MTA, Ferric sulfate and NaOCl gel groups, and 95.5% in the Biodentine group. Radiographic success was 100% for MTA and NaOCl gel, 95.5% for Biodentine, and 81.8% for FS (p < 0.05). Most failures occurred in first primary molars. MTA and Biodentine demonstrated high clinical and radiographic success, with Biodentine offering a faster procedure. NaOCl gel showed promising outcomes comparable to MTA. However, FS had lower radiographic success, indicating limitations for long-term use.Trial registration number: NCT07120321. Data: 08 August 2025. Retrospectively registered.