AI Tooth Identification for Intraoral 3d scans

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@shadabkhan92

With years of experience in building AI systems that work, we are confident we can meet and exceed your expectations for this project. Our expertise lies in implementing intelligent infrastructure within existing workflows to enable autonomous and real-time decision making. We have a comprehensive understanding of the stack you prefer - from Python with PyTorch or TensorFlow to clean interfaces like REST or CLI inference. We understand the importance of precision and speed in your project and rest assured, we are capable of delivering on both fronts. Our record of creating high-performing ML models, with an emphasis on accuracy, would be invaluable for achieving the ≥ 95% tooth-level detection recall and the ≥ 93% of correct numbering and type classification as per your acceptance criteria. More than just meeting these benchmarks, we aim to deliver a clean and maintainable code base you can easily integrate into your chairside software. With a deep understanding of a range of environments including edge devices, ERP workflows, and live sensor data, we have the capacity to put this AI model exactly where you need it. Put simply: when it comes to AI that goes beyond prototypes to meaningful production deployment, we are the right partner for the job.

@ashinnd84

Hello there, we are a team of AI developers and we can build your AI mechanism tools. Please, send me a message to discuss the work. Thanks Ashish Kumar.

@vishal1145

Your 95% recall target will fail in production if the model hasn't seen edge cases like partial eruptions, crowns, or missing teeth. I've built similar computer vision pipelines for medical imaging where a 2% accuracy drop translates to thousands of misdiagnoses. Before architecting the solution, I need clarity on two things: What's the resolution and file format of your 3D scans (STL, PLY, or proprietary)? And does your validation set include pathological cases like severe crowding, implants, or orthodontic brackets? These variables determine whether we need a hybrid 2D-3D approach or pure point cloud segmentation. Here's the architectural approach: - PYTORCH + POINTNET++: Process 3D mesh data directly using point cloud segmentation to maintain spatial accuracy that 2D projections lose, especially for overlapping molars. - OPENCV + ALBUMENTATIONS: Preprocess scan projections with augmentation pipelines that simulate lighting variations and occlusions to boost generalization by 15-20%. - FASTAPI + REDIS: Build a REST endpoint with request caching so repeated scans of the same patient don't re-run inference, cutting response time from 2s to 200ms. - FDI CLASSIFICATION LAYER: Train a secondary head on the segmentation backbone using anatomical priors (tooth position relative to midline) to enforce numbering logic and prevent impossible classifications. - ONNX EXPORT: Convert the final model to ONNX Runtime for 3x faster CPU inference if you need chairside deployment without GPU dependency. I've built 4 medical AI systems that passed FDA pre-submission review, including a retinal scan classifier that achieved 97.2% sensitivity. I don't take on projects where the validation data isn't representative of real-world conditions. Let's schedule a 20-minute call to review your dataset distribution and discuss failure modes before I commit to the benchmarks.

@mugencodes

Hi, hope you are well. I have extensive experience working with technologies such as Python, Machine Learning (ML), Java, Deep Learning, REST API, Software Architecture, and I’ve used them to build scalable, production-ready systems across multiple projects. I understand your goals and will deliver clear work on time while keeping you updated. we have many years of development experience in Python, Machine Learning (ML), Java, Deep Learning, REST API, Software Architecture and I have completed similar projects. Visit our website and check our work style and team members Looking forward to working with you, connect in chat or talk on a call. Regards, Jayabrata Bhaduri

@ssldevmaster

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@chandanm308

Its easy project—I made a similar system previously for a client and I can show a working demo for this. Let’s connect. For your use case, I would design a two-stage AI pipeline optimized for both accuracy and speed: 1. Tooth Detection (Localization) I’ll use a state-of-the-art object detection model (YOLOv8 / Detectron2) trained on intraoral datasets to detect each tooth as a bounding box. Preprocessing with OpenCV (contrast enhancement, normalization) will improve robustness across lighting and angle variations. 2. Numbering + Classification Once teeth are detected, a secondary classification model (CNN/Transformer-based) will: • Assign FDI or Universal numbering based on spatial positioning (quadrant mapping logic) • Classify tooth types (incisor, canine, premolar, molar, wisdom) This hybrid approach ensures high recall and precise labeling. 3. 3D Scan Support For intraoral 3D scans, I can extend this using point-cloud or multi-view image processing (e.g., projecting 3D → 2D views or using lightweight 3D CNNs depending on your data format).

@chiragm078

As an adept full-stack developer with a specific leaning towards AI and data processing, I have both the skills and experience you need for this project. My robust knowledge of Python and proficiency in PyTorch and TensorFlow will ensure the creation of a model that guarantees superior accuracy and speed. Moreover, my grasp on OpenCV for preprocessing allows for optimal data management in the workflow. Dealing with JSON deliverables is one of my specialties as well, so your desired output schema will be administered to flawlessly. Over my five years within a global landscape, I have sharpened my skills with projects that prioritize clear code, seamless functionality, and maximum performance - exactly like yours does. With a focus on delivering results that go beyond expectations, my dedication to end-to-end project delivery from ideation to deployment will be demonstrated as I create clear, concise documentation for all aspects of the work. Lastly, my background in designing APIs and endpoint systems in various frameworks would suit your requirements for tooth identification on a cloud instance perfectly. As an added plus, should we find it more efficient, I am fluent in designing alternative stacks as well. Working together on this exciting dental AI project would be an absolute thrill! I eagerly await the chance to discuss further details with you soon.

@shuvadeep003

Hi there, This is a highly specialized and interesting problem, and I have experience working with computer vision models for medical-style detection and classification tasks. My proposed approach focuses on accuracy + speed: * Use a two-stage pipeline: 1. Object detection (YOLOv8/Detectron2) to locate each tooth 2. Classification + numbering layer to assign FDI/Universal IDs and tooth type * Preprocessing with OpenCV (contrast normalization, ROI focus) to improve consistency across intraoral images * For numbering: apply a **spatial mapping algorithm** (arch segmentation + relative positioning) to ensure correct FDI/Universal assignment rather than relying only on classification * Training strategy: fine-tune on your dataset (critical for ≥95% recall target) with augmentation for robustness Deliverables: * Trained model (PyTorch-based) * REST API or CLI inference tool (FastAPI preferred) returning clean JSON (position, number, type) * Well-documented code + setup guide + test example Performance targets: * Optimized model (quantization/pruning if needed) to achieve ≤2s inference on mid-range GPU * Focus on maintainability for easy integration into your chairside system Data needs: * Annotated dataset (bounding boxes + tooth labels) * Sample edge cases (occlusion, mixed dentition, image variations) Availability: Immediate Happy to review sample data and validate feasibility before starting. Best regards, Shuvadeep

@prachig201

Hi! I'm a Data Science and AI practitioner with hands-on experience in Python, machine learning, deep learning, and computer vision. I've worked on ML pipelines during my internship at IIT Jammu and built real-world AI-powered applications, so I understand what it takes to deliver production-ready models, not just prototypes. For your intraoral scan project, I'd approach it using a fine-tuned object detection model (YOLOv8 or Faster R-CNN) trained on dental imaging datasets, combined with OpenCV for preprocessing. The model would output structured JSON with tooth positions, FDI/Universal numbering, and tooth type classification. I'd wrap it in a lightweight REST API (Flask) so it integrates cleanly into your chairside software. My plan to meet your benchmarks: ≥95% recall: achieved through careful augmentation and fine-tuning on domain-specific data ≥93% classification accuracy: using multi-label classification heads per detected tooth ≤2s inference: optimized with model quantization and efficient batching I'd also deliver clean documentation covering installation, input format, output schema, and a test example — exactly as you've outlined. One question: do you have an existing labeled dataset of intraoral scans, or would data sourcing/annotation be part of the scope? This will help me finalize the approach and timeline. Looking forward to working on this!

@fardeen2036

Hi, I've reviewed your project requirements carefully and I'm confident I can deliver a production-ready tooth detection and classification system that meets all three of your acceptance benchmarks. My Relevant Experience: I recently built a Medical Healthcare Assistant using ML with 96% AUC — involving real-time prediction, a Flask REST API backend, and deployment on Render. That project shares the same core pipeline you need here: model training → inference → API output → integration-ready JSON response. My Proposed Approach: • Use a fine-tuned YOLOv8 or Mask R-CNN model (PyTorch) for tooth detection — proven fastest for bounding-box + segmentation tasks • Map detections to FDI/Universal numbering using positional logic post-inference • Classify tooth type (incisor, canine, premolar, molar, wisdom) as a secondary classification head • OpenCV for preprocessing (contrast normalization, resizing, noise reduction on 3D scan renders) • Flask REST API endpoint returning clean JSON: { tooth_id, fdi_number, type, confidence, bbox } • Target inference ≤ 2s on mid-range GPU via model quantization if needed Deliverables I'll provide: Trained model with ≥95% recall and ≥93% classification accuracy REST API + CLI script for local/cloud inference Full documentation: installation, input format, output schema, test examples Clean, maintainable, well-commented code

@basanty37

Hello, I can build an AI system to detect each tooth in your intraoral images, assign correct FDI/Universal numbering, and classify tooth types. I will use a fast deep learning model (PyTorch + YOLO) with OpenCV preprocessing for high accuracy and speed. The system will return clean JSON output and run via a simple API or CLI. You will get a trained model, easy deployment, and clear documentation. I will optimize it to meet your accuracy (≥95%) and speed (≤2s) goals. I only need your dataset (labeled if available) to start. The solution will be clean, reliable, and ready for integration.

@mariamm1212

Hello, Building an AI system that can accurately detect and classify teeth in intraoral 3D scans is a complex computer vision problem, and this is exactly the type of challenge I’m confident working on. I specialize in Python-based machine learning and computer vision systems, with experience in designing end-to-end pipelines for image detection, classification, and structured output generation. Proposed approach: • Data preprocessing using OpenCV (enhancement, normalization, segmentation of intraoral scans) • Deep learning model development using PyTorch or TensorFlow for tooth detection and classification • Integration of FDI/Universal numbering logic aligned with model predictions • Optimization for high accuracy and fast inference (≤2s per image requirement) • Deployment via lightweight REST API or CLI for easy integration • Iterative validation and tuning to meet required performance benchmarks I focus on building scalable, production-ready AI systems with clean architecture, reproducible training workflows, and performance-driven optimization. I am comfortable working closely with datasets, refining model performance, and iterating until the system achieves the required accuracy and reliability for real-world use. Looking forward to contributing to this advanced and impactful AI project. Best regards, Mariam

@omarn30

I can build a high-accuracy tooth detection and classification system tailored to intraoral images, optimized for both precision and speed. My approach will combine a state-of-the-art object detection model (e.g., YOLOv8 or Faster R-CNN) with a structured post-processing layer to assign correct FDI/Universal numbering based on spatial relationships. First, I’ll preprocess your dataset (normalization, augmentation, annotation validation) to ensure robustness across lighting, angles, and variations. Then, I’ll train a detection model to localize each tooth and classify its type (incisor, canine, premolar, molar, wisdom). A custom numbering algorithm will map detected teeth to their correct positions using geometric constraints and arch alignment. To meet your accuracy targets, I’ll apply targeted augmentation and fine-tuning, along with evaluation on your validation set. For performance, the model will be optimized (ONNX/TensorRT if needed) to ensure ≤2s inference on a mid-range GPU. Deliverables include a trained model, a clean Python-based REST API or CLI for inference, and well-documented code with clear input/output JSON schema. Documentation will cover setup, usage, and a quick test example. I’ll ensure ≥95% detection recall and ≥93% correct numbering/classification, with a clean, maintainable pipeline ready for integration.

@CreativeAIpro

I propose a two-stage pipeline focused on accuracy and speed. 1. Detection Fine-tune a pre-trained instance segmentation model (YOLOv8-seg or Mask R-CNN) to detect each tooth with bounding boxes or masks. Use OpenCV for preprocessing (contrast normalization, lighting correction). 2. Classification + Numbering Crop each detected tooth and classify it using a lightweight CNN (EfficientNet or ResNet) into incisor, canine, premolar, molar, or wisdom. Assign FDI/Universal numbers using spatial rules: separate upper/lower arches, then sort teeth left-to-right. Optional geometric or graph-based refinement improves consistency. 3. Data Requirements Annotated intraoral images with tooth regions, type labels, and numbering. If limited, apply augmentation (rotation, blur, brightness). 4. Inference PyTorch pipeline exposed via FastAPI or CLI. Returns JSON with bbox, type, number, and confidence. 5. Performance Use mixed precision and ONNX/TensorRT to achieve ≤2s per image. With ~1–3k labeled images, expect ≥95% detection recall and ≥93% classification accuracy. Deliverables Trained model, API/CLI, documentation, and validation results. Risk Numbering in complex views, mitigated via spatial modeling.