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  • Küçük Resim
    Yayın
    Cross-domain generalization of deep learning architectures for cephalometric landmark detection: A dual-dataset and multi-device benchmark
    (MDPI Publishing, 2026) Allaf, Ferdi; Özcan, Mustafa
    Background/Objectives: Deep learning models for cephalometric landmark detection report near-ceiling accuracy on single benchmarks, yet most are trained and tested on the same dataset. Whether the best in-domain architecture remains best out-of-domain has not been systematically quantified. Methods: Four architecture families (heatmap CNN, two-stage cascade, coordinate-regression Vision Transformer, pretrained ResNet-50) were each trained on two independently sourced datasets—ISBI 2015 (400 images, one device) and Aariz (1000 images, seven devices)—and evaluated on both, over their 19 shared landmarks in millimeters. All axes used three seeds (mean ± SD): the cross-dataset matrix, leave-one-device-out shift, balanced joint training, a pretrained-versus-scratch ablation, and a landmark-level breakdown. Results: In-domain mean radial error (MRE) was 2.47–3.04 mm (Aariz) and 4.12–5.55 mm (ISBI); cross-dataset error rose steeply (Generalization Drop—the relative increase in error out-of-domain—155–426%). The most accurate model in-domain (a from-scratch heatmap CNN, 2.47 mm) showed the largest drop, and every pretrained estimate fell below every from-scratch estimate (family means 248% vs. 380%): in-domain ranking did not predict cross-domain ranking. Leave-one-device-out revealed reproducible device-specific shift (held-out MRE 1.89–7.94 mm). Inter-observer variability was 0.53 mm, so cross-domain errors were 15–44× the human band. Balanced joint training reduced the cross-domain gap for all four architectures (both domains ≈ 2.1–3.5 mm) without harming in-domain accuracy. Pretraining more than halved cross-domain error from Aariz to ISBI (7.73 vs. 16.36 mm) but not in reverse, supporting the mechanism in one direction rather than uniformly. A-point, B-point, Nasion, and Menton all exceeded the 2 mm clinical threshold out-of-domain, including points localized to sub-millimeter accuracy in-domain. Conclusions: Single-dataset accuracy substantially overstates clinical generalizability, and the best in-domain architecture is not the most transferable, so in-domain leaderboards are an unreliable basis for selecting a model for clinical deployment; balanced multi-source training recovers most of the loss across the architectures tested.
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    Dijital ortodontide görüntüleme ekosistemi: CBCT, intraoral tarayıcılar ve 3D model doğruluğu
    (Yaz Yayınları, 2026) Allaf, Ferdi; Allaf, Ferdi
    Ortodonti, kraniyofasiyal morfolojiyi anlama çabasında uzun süre statik ve iki boyutlu (2D) verilere bağımlı kalmıştır. Geleneksel tanı, üç boyutlu biyolojik bir gerçekliği iki boyutlu gölgeler (panoramik ve sefalometrik radyografiler) ile iç yapıyı görünür kılmayan alçı modeller üzerinden yorumlama çabasıydı; klinisyenin bilişsel yükü, teknolojik yetersizliği telafi eden asıl araçtı (Halazonetis, 2005). Günümüzde bu süreç veriye dayalı nesnel bir simülasyona dönüşmüştür: kron yüzeyleri intraoral tarayıcılarla mikron düzeyinde, kök ve iskeletsel anatomi CBCT ile milimetrik hassasiyette yakalanır; bu verilerin entegrasyonu tanıyı statik bir "anlık kayıt" olmaktan çıkarıp sonuçların öngörülebildiği bir "dijital ikiz" modeline taşır (Plooij ve diğerleri, 2011).
  • Küçük Resim
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    Does thermoforming setup affect thickness in thermoplastic orthodontic appliances? A comparison of single vs. dual-model fabrication
    (MDPI Publishing, 2026) Allaf, Ferdi; Arslan, Meriç; Özcan, Mustafa; Erdem, Buket
    Thermoforming remains the predominant fabrication route for clear thermoplastic orthodontic appliances, yet it reduces and redistributes sheet thickness, which governs force delivered to teeth. Simultaneous thermoforming of two models may reduce fabrication time and material waste; however, its effect on final appliance thickness has not been evaluated. This in vitro study compared appliance thickness after thermoforming over a single model or two models simultaneously, using ten commercial aligner brands with different polymer composition and initial thickness. Thickness was measured with a digital caliper at anterior, canine and posterior sites, and analyzed using three-way ANOVA with Tukey post hoc tests. A significant three-way interaction (measurement point × number of models × brand; p < 0.001) was found. Significant single- versus dual-model differences emerged in some brand–region combinations; in most, dual-model appliances were thicker than single-model appliances. Four brands showed no significant difference at any site. Within the limits of this study, simultaneous fabrication of two appliances in a single thermoforming cycle produced appliances with thicknesses comparable to, and often slightly greater than, those fabricated over a single model. Simultaneous thermoforming maintained comparable appliance thickness and may improve manufacturing efficiency.
  • Küçük Resim
    Yayın
    Ortodonti uygulamalarında yapay zeka:Tanıdan tedavi planlamasına kanıta dayalı yaklaşım
    (Vizetek Yayıncılık, 2026) Allaf, Ferdi; Doruk, Ercan Cenk
    Modern diş hekimliği disiplinleri arasında dijitalleşme süreçlerine en hızlı ve dinamik entegrasyon gösteren alan kuşkusuz ortodontidir. Üç boyutlu görüntüleme modaliteleri, intraoral tarama teknolojileri, bilgisayar destekli tedavi planlama yazılımları ve şeffaf plak (aligner) sistemleri, ortodontik teşhis ve tedavi ekosistemini kökten değiştirmiştir. Bu teknolojik evrimin tepe noktasında yer alan yapay zekâ (YZ) uygulamaları, günümüzde sadece klinik iş akışlarını hızlandıran otomasyon araçları olmaktan çıkmış; klinisyene kanıta dayalı, öngörülebilir ve kişiselleştirilmiş bir karar destek mekanizması sunan temel bir unsur haline gelmiştir (Kapila ve ark., 2021).
  • Kapalı Erişim
    Yayın
    Production-phase carbon footprint of photopolymer resin workflows for direct-print orthodontic aligners: A screening carbon-footprint assessment
    (MDPI Publishing, 2026) Özcan, Mustafa; Allaf, Ferdi
    Direct-print aligners are widely presented as the environmentally preferable alternative to thermoforming, but which part of the process governs the carbon released during manufacture has not been established. Four commercial printer–resin workflows were modelled: Graphy Tera Harz resin on a Uniz NBEE (A) and an Asiga MAX 2 (B), the LuxCreo iLux Pro as a closed chain (C), and Graphy resin on a DentaFab Sega Pro (D). Energy, material and throughput data came from the manufacturers. Production-phase CO2e per aligner was propagated through 10,000 Monte Carlo draws, with the resin emission factor sampled across its full published range (3.0–6.5 kg CO2e/kg) and the thermoformed comparator represented as a distribution. Mean footprints were 0.043, 0.036, 0.089 and 0.020 kg CO2e per aligner (95% uncertainty intervals were 0.032–0.056, 0.026–0.047, 0.077–0.101 and 0.016–0.025), against a thermoformed median of 0.190. Direct printing was the lower of the two in every paired iteration. Resin dominated, contributing 41–79% of the total; solvent added a further 41% in the one workflow using it, while electricity of all kinds accounted for 19–36%. Thermoforming would match direct printing only at series lengths of 45 to 202 aligners, far above the 14–30 of clinical practice; under the most unfavourable resin factor the margin narrowed but did not close. Material efficiency, not energy sourcing, is where reductions are available.