Dental X-Ray Grading Model

@AwaisChaudhry

Hello, I understand you have a curated set of about 700 post-operative periapical X-rays and want a Python-based CV model (TF or PyTorch) that outputs a numeric score for grading and a concise, rubric-driven feedback report. I will architect a robust, trainable pipeline that handles preprocessing, data augmentation, model training, evaluation, and reproducibility. I will deliver an inference script that processes a single image or a folder, and writes results to JSON or CSV, with clear environment setup, architecture summary, and step-by-step reproducibility notes so you can continue training as data grows. The model will assess root-canal pathway accuracy, cleanliness, and overall completion quality, and translate findings into tutor-style comments per your rubric. If helpful, I will include Grad-CAM style overlays to justify scores. What I will deliver: - Data preprocessing, train/val/test split, and augmentation plan suited to dental radiographs. - Architecture overview (e.g., backbone, multi-task heads for score and feedback, loss functions, and explainability). - Training scripts with logging, checkpoints, and evaluation against your faculty benchmark. - Inference tool to batch process folders, export JSON/CSV results, and reproduce results. - Environment setup, dependencies, and a short summary of the pipeline for future extensions. What is the minimum acceptable granularity for the score and how should the rubric be encoded for automatic mapping to grades? Best reg

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Computer Vision Model for Evaluating Dental X-rays ❇️ Hi My Friend, I hope you are doing well. I reviewed your project needs and see you are looking for a computer vision model to assess dental X-rays. You don’t need to look any further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects in medical imaging. I will design, train, and validate the model using Python with TensorFlow or PyTorch, ensuring it meets your requirements. I will provide a numeric score and a concise feedback report on the X-rays. My approach includes detailed analysis of the root-canal pathway accuracy, cleanliness, and overall quality. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in computer vision and machine learning. My skills include model training, data analysis, and image processing. I also have a strong grip on TensorFlow and PyTorch, ensuring a solid foundation for your project. ➡️ Let's have a quick chat to discuss your project in detail and I can show you examples of my previous work. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Python Programming ✅ TensorFlow ✅ PyTorch ✅ Computer Vision ✅ Model Training ✅ Data Analysis ✅ Image Processing ✅ JSON/CSV Output ✅ Feedback Generation ✅ Environment Setup ✅ Medical Imaging ✅ Segmentation Techniques Waiting for your response! Best Regards, Zohaib

@AITSoft

Hello, I can design, train, and validate a Python-based deep learning model (TensorFlow or PyTorch) to automatically score post-operative periapical X-rays and generate tutor-style feedback. I will use your rubric to map radiographic findings to a numeric score and concise comments, covering root-canal pathway accuracy, cleanliness, and overall completion. The pipeline will include: data preprocessing (normalization, alignment, data augmentation, and class-imbalance handling), a clear train/val/test split, and performance metrics aligned to your 85% benchmark. I will propose a compact architecture (e.g., a CNN backbone with task-specific heads) plus optional Grad-CAM overlays for explainability. The model will output a JSON/CSV with the score and feedback per image or batch, and an inference script to process a single image or a folder, plus environment setup instructions and a short reproducibility summary. I will package training and inference code so you can continue adding data and re-train easily. What is the preferred grading rubric format and the exact fields you want in the output JSON/CSV (e.g., score, per-criterion breakdown, feedback headings) for seamless integration into your spreadsheet? Best regards, Shamshad

@softsolution2000

I have extensive experience in developing computer vision models for medical imaging tasks. Your project of creating a Dental X-Ray Grading Model aligns perfectly with my skill set. To ensure I understand your requirements correctly, you need a Python model to evaluate post-operative X-rays, providing a numeric score and feedback report based on specific criteria. I am confident in my ability to meet your benchmarks and provide consistent results. Once we discuss the full project scope, I am open to adjusting the budget accordingly. Please review my profile showcasing my 15 years of experience. Let's discuss the details and get started on this project. Looking forward to your response.

@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

@Vasilchenko

I can build and validate a TensorFlow/PyTorch computer-vision model that scores post-operative periapical X-rays, aligns outputs with your grading rubric, and generates tutor-style feedback plus JSON/CSV exports for single images or batches. My experience with deep learning, image pipelines, and explainability methods (e.g., Grad-CAM-style overlays) will help ensure reproducible performance and clear, rubric-based comments. https://www.freelancer.com/u/Vasilchenko Will your rubric and faculty benchmark scores be available per-image so we can train and evaluate with case-level labels?

@Herculesz

Hi, this project to automate grading of dental X-rays with a feedback report is a precise application of computer vision and ML model engineering. The main engineering risk lies in achieving consistent, reproducible scoring aligned with faculty benchmarks under variable image conditions. I usually structure such systems by separating preprocessing, model inference, and postprocessing layers, ensuring modularity and ease of retraining as data grows. I've built several production-grade AI systems with complex evaluation criteria and reproducibility guarantees. The AI-Driven Marketing Suite Development project reflects my experience in full-cycle AI model development, and the Python Bug Localization project demonstrates my ability to deliver reproducible, well-documented ML pipelines. I recommend designing a robust preprocessing pipeline to normalize image inputs, a modular scoring architecture that maps rubric sub-criteria to outputs, and integrating explainability overlays optionally. Systems I design emphasize long-term maintainability with clear environment setup, reproducible training, and inference scripts. I can start by outlining the preprocessing steps and scoring pipeline architecture for your review. Thanks, Hercules

@chraheelbhatti

With a solid 10+ years of experience as a Senior Machine Learning Engineer and PhD in AI & Machine Learning, I am confident in my abilities to tackle your Dental X-Ray Grading Model project. My expertise includes Computer Vision and Deep Learning, two fields crucial for a task like this. Furthermore, I have successfully designed, trained, and deployed high-accuracy CNN models in the healthcare domain which positions me strongly to complete your project. My proficiency isn't limited to just using Python and TensorFlow/PyTorch for model building – I ensure that my work is field-tested and user-friendly. Hence, I will not only deliver a model that fits into your system seamlessly but also provide clear, concise instructions for easy reproduction and further training as you merge new data. Another aspect that enhances my credibility is my experience with medical imaging- whether it's segmentation or explainability techniques like Grad-CAM. This opens up the potential for providing detailed visual overlays justifying each score produced by the model - providing a welcome-added bonus you mentioned. All these skills along with compatibility ratings(Rectifying commenting), would validate my fit for the job, helping me deliver an optimized AI assistant for your specific goals.

@Feriver

Hello, I understand you need a Python-based computer vision system to automatically grade ~700 post-operative periapical X-rays after root canal treatments, producing both a numeric score and structured feedback aligned with your dental rubric. The model must evaluate canal accuracy, cleanliness, and overall treatment quality, with at least 85% agreement against faculty benchmarks and stable outputs across runs. I will build a deep learning pipeline using PyTorch or TensorFlow with a medical imaging backbone (CNN/transfer learning). The system will include preprocessing (normalization, augmentation, contrast balancing), training on your labeled dataset, and a scoring head that outputs a numeric grade mapped directly to your rubric. A secondary rule-mapping layer will generate concise, structured feedback aligned with rubric criteria. For interpretability, I can integrate Grad-CAM overlays to highlight image regions influencing the score. The deliverable will include a reproducible training pipeline, evaluation framework (cross-validation, stability checks), and an inference script supporting single-image and batch processing, exporting results to JSON/CSV. Full setup instructions and architecture documentation will be included so you can extend training as new data becomes available. Thanks, Asif

@corpmember29

MEDICAL IMAGING AI REQUIRES MORE THAN JUST MODEL TRAINING — IT DEMANDS CONSISTENCY, EXPLAINABILITY, AND CLINICALLY RELIABLE OUTPUTS. With 12+ years of experience in AI, computer vision, deep learning, and healthcare-focused software systems, we can develop a Python-based dental X-ray assessment model capable of generating both numeric grading scores and rubric-aligned feedback reports from post-operative periapical radiographs. We can deliver: • TensorFlow or PyTorch-based CV pipeline • X-ray preprocessing & normalization workflows • Multi-criteria scoring system based on your grading rubric • Automated feedback/comment generation • Batch & single-image inference scripts • JSON/CSV export outputs • Reproducible training environment & documentation • Validation/testing pipeline with benchmark reporting • Grad-CAM or explainability overlays (optional bonus) Recommended Approach: CNN/Hybrid architecture with transfer learning, segmentation-assisted feature extraction, rubric-mapped classification/regression layers, and explainability visualization for score justification. If you can share sample X-rays and the grading rubric, I can immediately suggest the most suitable architecture and expected accuracy strategy for achieving your faculty benchmark targets.

@devendrathakur12

i’ve done very similar recently on a medical imaging review pipeline using PyTorch, Grad-CAM overlays, and rubric-based scoring with structured feedback generation. Are the faculty scores already normalized across evaluators, or do we need inter-rater calibration first? Do your X-rays include annotations/masks for canal paths, or only final scores and comments? I’d use a hybrid pipeline with segmentation plus scoring instead of a single classifier. That improves consistency and makes feedback explainable for faculty review. I’d also add deterministic training seeds and fixed augmentations because your ±2 repeatability requirement is important for grading reliability. First I’ll audit label quality, rubric structure, and class balance across the 700 images. Then I’ll build preprocessing, train the segmentation/scoring models, and map predictions into rubric-based feedback text. Final step is batch inference tooling, Grad-CAM validation overlays, and reproducible training documentation. Best, Dev S.

@TheWorkingGroup

Hi! I have worked extensively with dental radiographs. I'd love to show my work and discuss your needs with you. Best wishes, Salaar Khan

@DeveloperStation

Hi, We’ve developed a similar model for a dental startup, where we used X-rays to assess root canal quality and provide feedback to dentists. We trained the model with labeled data from dentists, ensuring it learned to identify critical features like canal visibility and filling quality. For your project, we can use a combination of computer vision and NLP to generate concise feedback based on the model’s output. We can also implement Grad-CAM to highlight areas of interest in the X-ray images. We’re fully equipped to handle all aspects of this project, from model development to web app creation for easy access. Let’s schedule a 10-minute call to discuss your requirements in more detail and see if I’m the right fit for your project. I’m eager to learn more about your exciting project. Best regards, Adil

@nrajuu07

As a seasoned Machine Learning Engineer with a strong focus on medical image analysis and computer vision, I am perfectly equipped to tackle your project. My experience with diverse modalities such as MRI, CT, and X-ray combined with my knowledge of state-of-the-art deep learning models including UNet and ViT make me an excellent fit to design, train, and validate the grading model you require. More than just delivering scores and numbers, I understand the importance of actionable feedback in medical scenarios. I'm confident I can develop a model that not only produces accurate grades as per your faculty's benchmark but also provides informative feedback based on the rubric you supply. The use of explainability techniques like Grad-CAM for visual overlays justifying each score would be an additional advantage. Therefore, not only will I provide a proficient inference script that generates scores and feedback accurately but also clear instructions for environment setup, an architecture summary, preprocessing steps so you can reproduce results and continue training with new data. Let's bring on the precision in dental X-ray grading together!

@seefattechnologi

I checked your project description -------I believe I can do this project in an efficient and professional manner. I am highly proficient to work on this project . I am Passionate PYTHON/Full stack developer having rich experience with all the latest technologies with so many successful Tasks. I have some queries to give you accurate time and price Please ping me to get started and provide you great results. Thanks

@tushortz

Hello, I understand you need a Python-based computer vision model to automatically grade dental X-rays, providing a numeric score and concise feedback report. This aligns perfectly with my experience in developing robust backend systems and applying machine learning techniques. I'm Taiwo, a UK-based Senior Software Developer with 10 years of experience and a Master’s in Cyber Security. My background includes working with companies like IBM, UK Government, BMW, and Sky, where I focused on building secure, scalable, and reliable platforms. I can develop a model using TensorFlow or PyTorch that assesses X-rays based on your rubric, delivering a score and feedback in JSON/CSV format. I will ensure at least 85% agreement with your faculty benchmark, consistent scoring across reruns, and feedback text aligned with your rubric headings. Relevant projects: * **IMS Team:** Built a project and timesheet management system that improved collaboration and workflow efficiency. * **Equity Share:** Developed backend functionality for a US real estate crowdfunding platform with a focus on secure, scalable application logic. My approach includes designing, training, and validating the model, packaging an inference script, and providing clear documentation for reproduction and future training. I can also incorporate explainability techniques like Grad-CAM for visual overlays. If you're interested in discussing the rubric details and project scope further, please let me know when you are available. I can start immediately.

@quan9824

Hello. I’ll build a PyTorch model to assess root canal X‑rays – outputs numeric score + rubric‑based feedback. Architecture: Vision Transformer (overall quality score). U‑Net + YOLO (segmentation of pathway, detection of voids/cleanliness). Grad‑CAM overlays (bonus). Deliverables: Trained model (≥85% agreement with faculty). Inference script (single/batch → JSON/CSV). Setup guide, architecture summary, retraining instructions. Consistent scoring (±2 points across runs). Previous medical imaging experience – ready to discuss your rubric. Doan

@Ekarthaan

Hi Johny E., Last week I delivered a very similar dental periapical X‑ray grading model, so I’m confident I can handle this really well. I’ll use Python + PyTorch with transfer learning (e.g., EfficientNet) in a multi‑head setup: a calibrated regression head for the numeric grade, plus rubric‑aligned heads for pathway accuracy, cleanliness, and completion. Preprocessing will include denoise, intensity standardization, CLAHE, and a tooth ROI finder (light U‑Net/detector) so the model focuses on the treated area. I’ll add Grad‑CAM overlays to justify each score. For ±2 point stability I’ll fix seeds, use cross‑val, and test‑time augmentation averaging. We’ll target ≥85% agreement on a held‑out set. i would like to know the below. - Are images DICOM (with metadata) or PNG/JPG, and is resolution consistent? - Do you have per‑criterion faculty labels or only overall scores per X‑ray? I think we should. - Use Docker + pinned requirements and config‑versioned seeds for exact reruns and long‑term reproducibility. - Curate a tiny expert‑labeled subset (canal/obturation masks) to boost ROI quality and explainability. Lets follow a plan like this. 1) I review data and rubric, define labels/splits, and lock acceptance checks. 2) I build preprocessing + ROI finder and a clean baseline; sanity‑check on samples. 3) I train, calibrate, and validate across 3 runs; tune until targets are met; add overlays

@ashinnd84

Hello there, we are a team of senior AI experts, Machine learning, Computer vision, Full Stack Web and Mobile App Developers and we can do this project in no time. Please, send me a message to discuss the work. Thanks Ashish Kumar.