Platform switch hybrid zygoma implants improve prosthetics and marginal bone protection after extra-sinus placement.
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The manual identification of dental implant systems on radiographs is time-consuming, operator-dependent, and prone to diagnostic inaccuracies, particularly for patients where clinical documentation is lacking. The increasing variety of implant designs further complicates identification in prosthetic and surgical practice. The purpose of this study was to develop and evaluate a deep learning-based model for the automated identification of 7 implant systems (Adin, Dentium, Dionavi, Make It Simple (MIS), Nobel, Noris, and Osstem) using panoramic radiographs and periapical radiographs in an effort to enhance diagnostic efficiency and support clinical decision-making in prosthodontic care. A total of 4677 anonymized radiographic images with 8189 implants were curated and annotated using Roboflow with bounding boxes outlining fixture components. The preprocessing involved normalization, resizing to 640×640 pixels, and geometric augmentation (rotation, cropping, and blurring) to handle class imbalances. You Only Look Once (YOLO) v10 architecture, implemented with PyTorch, using CSPDarknet and PANet for multiscale feature fusion, was used to optimize real-time detection. Transfer learning used pretrained weights, with training for over 500 epochs (batch size: 32) on NVIDIA T4 GPUs. Data partitioning involved an 80:10:10 ratio (training: validation: testing), with performance evaluated using precision, recall, F1-score, and mean average precision (mAP). The model achieved a mAP of 98.3%, with mean precision, recall, and F1-score values of 93%, 86%, and 89%, respectively. Osstem implants demonstrated maximum discriminability (99% precision, 95% recall). In contrast, Nobel implants exhibited low recall (72.7%), attributed to the sparsity of the dataset (564 samples for Nobel compared with 2320 for Osstem) and similar radiopacity patterns. The YOLOv10 model demonstrated good performance in identifying dental implants, showing clinical promise for minimizing prosthetic mismatches. Subject to ethics and regulatory approvals, additional improvements involving 3-dimensional imaging and heterogeneous datasets may add precision and validate artificial intelligence as an evidence-based advance in implant dentistry. Implant identification is a pressing concern in dental implantology, and artificial intelligence (AI) has been evaluated for this purpose. YOLO, a state-of-the-art object detection model, is suitable for medical imaging; therefore, this study assessed YOLOv11-the latest iteration-for identifying 10 implant types in Indian clinical settings and compared its accuracy to that of dental professionals. A dataset of 3,161 radiographs, comprising both periapical and panoramic images of 10 implant types, was annotated and used to train and test YOLOv11. Training was performed on Google Colab using an NVIDIA Tesla T4 GPU (16 GB VRAM). A random sample of 200 radiographs was selected from the test dataset and presented to 50 dental practitioners for implant identification. Their responses were analysed and compared, using the chi-square test for statistical significance. YOLOv11 achieved precision of 0.87, recall of 0.85, an F1-score of 0.86, and an mAP50 of 0.899. The model achieved excellent classification accuracy for Adin (95%), MIS (94%), Bego (92%), ITI (96%), and Bicon (97%). Moderate accuracy was noted for Noris (82%), Osstem (85%), AlphaBio (88%), Dentium (77%), and Bioline (75%). YOLOv11 demonstrated higher overall accuracy and consistency than dental professionals. Dentists' accuracy ranged from 27% to 49%, whereas that of YOLOv11 ranged from 92% to 100%. YOLOv11 recognised most implant classes with over 90% accuracy, surpassing traditional manual techniques in implant detection. Although the model is dependable and efficient, certain aspects require improvement. The study also emphasises the significance of a region-specific approach for clinical relevance. The aim of our studdy is clinical evaluation of Platform switch hybrid zygoma implants. 117 zygomatic implants were followed up during this time. They included 55 Brånemark System zygoma implants, 38 Noris implants, and 24 novel iRES hybrid implants with platform switch. Bone quality and quantity are the prerequisite for successful implant treatment. Zygomatic implants are intended for patients with severely resorbed maxilla that cannot accommodate conventional implants without prior extensive bone grafting. Such regenerative procedures, like sinus lifts, prolong implant rehabilitation to several months (12-18). Furthermore, extensive grafts are less predictable showing varying degrees of graft resorption. Zygoma implants enable full, often immediate, reconstruction of the upper dental arch without the need for sinus lift treatment. The original zygoma protocol runs the implants through the sinus, requires general anesthesia, and positions the prosthetic platform of the implants on the palate, which makes prosthesis cumbersome. It also induces risk for post-op sinusitis. Extra-sinus approach with novel zygoma hybrid implants bypasses sinuses and positions the implant prosthetic platform on the crest allowing for same good prosthetics as on conventional dental implants. Furthermore, crestal threads and a platform-switch, of the novel zygoma design, increase implant anchorage and minimize marginal bone loss. The study presents evolution of zygoma implant rehabilitation protocol and zygoma implant design in our clinical practice over 15 years (2004-2019). Extra-sinus zygomatic implant placement lowers the risk of post-op sinusitis and makes procedure possible to be done in local anesthesia. The most common diagnosis for pediatric thrombocytopenia is immune thrombocytopenia. Nevertheless, in atypical cases, the hypothesis of an inherited thrombocytopenia has to be investigated. We report a series of cases of a newly described entity, genetic thrombocytopenia with mutation in the ankyrine 26 gene, diagnosed from the exploration of five pediatric cases of thrombocytopenia. This entity is characterized by a moderate thrombocytopenia with normal mean platelet volume, and poorly bleeding. Its transmission is autosomal dominant. Final diagnosis is made by sequencing of a short DNA region of ANKRD26 gene. This pathology can be considered as an hematological malignancy predisposition syndrome. We report the first cohort of pediatric patients diagnosed with thrombocytopenia with mutation in the ankyrine 26. The aim is to underline the specificities of this entity in children and bring it to the knowledge of pediatricians who may be in first place to manage these patients. • Genetic thrombocytopenia with mutation in the ankyrine 26 gene is a recently described entity, which seems to be considered as a predisposition for hematologic malignancies. • The first cohort has been reported in 2011, by Noris et al., in 78 Italian adult patients. What is New: • We describe clinical and biological features of the first pediatric cohort diagnosed with genetic thrombocytopenia with mutation in the ankyrine 26 gene. • It seemed important to consider the pediatric specificities of this entity to enable pediatricians to investigate, diagnose, and manage pediatric patients and their families. Noris and Remuzzi discuss a new study showing an association between atypical haemolytic uremic syndrome and a hybrid complement gene,CFH/CFHL1. Epidemics of tomato yellow leaf curl have occurred annually in greenhouse- and field-grown tomato (Lycopersicon esculentum Mill.) crops in southern Spain since 1992 (2). The nucleotide sequences of two tomato yellow leaf curl virus (TYLCV) isolates from this region, TYLCV-M (GenBank accession no. Z25751) and TYLCV-Alm (L27708), have been determined and these isolates are closely related to isolates reported from Italy (X61153 and Z28390), suggesting the existence of a geographical cluster of closely related TYLCV isolates in the Western Mediterranean Basin (2