AI Fetal Ultrasound Biometry Analysis System

@pkundu25

I am an experienced AI developer. Your job caught my eye and looks to be quite interesting to me as I developed Fetal brain abnormality detection using Computer Vision techniques in recent past. I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am confident that I will be able to help you by developing AI-based solution for Ultrasound plane detection and classification, semantic segmentation etc. as per your requirements. Similar work done in the past: - Fetal brain abnormality detection - Vehicle detection and classification - AI Powered Copilot for Text2SQL Query - Image search - Semantic search engine - OCR image recognition Relevant Skills: - Python - Agentic AI - Computer Vision/SOTA architectures - Transfer Learning - GPT4o/Gemini/Llama3.2 - LangChain - MySQL - TensorFlow - Google Colab - OpenCV Let's have a chat to understand the project objective and the dataset in details. I assure you to deliver the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@avinashdubeyg209

Noticed your focus on ultrasound plane detection and ellipse fitting for fetal biometry. Recently led a project utilizing CNNs for medical image segmentation, enhancing accuracy in anatomical structure identification. Curious about your dataset: does it include varying gestational ages to ensure robust model performance across different developmental stages? Let me know if you’re open to discussing how I can integrate a tailored deep learning approach into your system. Can start today or adjust to your timeline.

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe)& I can help you take your fetal ultrasound biometry system from a baseline model to a production-grade, clinically safe pipeline delivered in phases (starting with HC18 head biometry, then expanding to AC/FL once data + validation are in place) Approach: Phase 1 : >>DICOM ingest + anonymization + preprocessing (windowing/normalize,tiling for high-res) >>Plane QC(accept/reject head plane) >>U-Net/DeepLab segmentationof skull/head region >>Robust ellipse fitting-> deterministic HC + derived BPD/OFD + head area >>Quality scoring + consistency checks(mask plausibility,ellipse residuals,size bounds) >>Explainability: Grad-CAM/overlay(original,mask,ellipse) >>Rule-based PDF reports(patient/doctor/technical) + audit logs (model version,runtime) Phase 2 (Abdomen/Femur): >>Extend plane detection (abdomen/femur) >>Dedicated segmentation/landmarks for AC + FL >>EFW + percentiles (INTERGROWTH/Hadlock) + calibrated confidence Phase 3 (Production hardening): >>Uncertainty calibration,monitoring,bias/fairness checks >>Active-learning loop from clinician corrections >>Traceability: immutable audit trail (inputs->preprocess->model->outputs) Relevant experience: >>Built medical/vision-style pipelines: segmentation + geometry extraction + strict QA gates >>Shipped end-to-end ML systems with FastAPI backends,logging/metrics & reproducible inference >>Experience with OCR/overlay/report generation & structured outputs for downstream workflows

@kamran2012

Hello there, I will build the two-phase pipeline — plane classification, U-Net segmentation, ellipse fitting, and automated HC/BPD/AC/FL extraction — with confidence-calibrated uncertainty on each measurement. For multi-task learning, I will share a single encoder across plane detection and segmentation heads, then add task-specific decoders. This cuts inference time and improves feature reuse across correlated biometry targets. Questions: 1) Do you have annotated data beyond HC18 and FETAL_PLANES_DB, or will labeling be part of scope? 2) Is the regulatory audit trace a documentation requirement now, or a future phase? Looking forward to talking through the details. Kamran

@Ramk068

Hi, I’ve carefully reviewed your requirements for the fetal ultrasound biometry analysis system and I understand the full pipeline you’re aiming to build. I have experience working with machine learning and AI-based systems, and I’m comfortable implementing computer vision pipelines including classification, segmentation, and measurement extraction. For this project, I can deliver: • Ultrasound plane detection (head, abdomen, femur) • Semantic segmentation using CNN/U-Net architectures • Biometry extraction (BPD, HC, FL, AC, etc.) • Measurement validation and consistency checks • Structured output/report generation I focus on building clean, modular systems so each component (classification, segmentation, measurement) is reliable and extensible. I’m also comfortable working with medical imaging datasets and can adapt the pipeline based on dataset characteristics and requirements. We can align on dataset availability and exact scope before starting to ensure smooth execution. Looking forward to working with you.

@abdf2010

Hey, I liked your project, AI Fetal Ultrasound Biometry Analysis System and believe I can help you with the project. With my background in Machine Learning (ML), Computer Vision, Deep Learning, I'm confident I can meet your requirements. Would be glad to go over specifics if you're interested.

@MuhammadAsad108

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Data Curation & Preprocessing ⟶⟶ Plane Detection & Classification ⟶⟶ Semantic Segmentation (U-Net) ⟶⟶ Biometry Extraction (Geometry + Ellipse Fitting) ⟶⟶ Measurement Validation & QA ⟶⟶ AI Explainability & Reporting ⟶⟶ Deployment & Continuous Learning Loop Key Highlights ✔ End-to-end AI pipeline — from ultrasound image input to fully automated fetal biometry outputs. ✔ Plane detection & classification — robust CNN models to identify head, abdomen, and femur views accurately. ✔ Advanced segmentation — U-Net / multi-task architectures for precise anatomical structure extraction. ✔ Accurate biometry calculations — HC, BPD, AC, FL, OFD, EFW with geometry-based ellipse fitting. ✔ Multi-task learning model — single backbone predicting multiple measurements efficiently. ✔ Confidence-aware outputs — calibrated uncertainty estimation for clinical reliability. ✔ Scan quality scoring — automatic validation of image quality and measurement consistency. ✔ Explainability layer — Grad-CAM visual overlays to highlight model decision regions. ✔ Longitudinal growth tracking — analysis across multiple scans with trend modeling. ✔ Risk & anomaly detection — comparison against INTERGROWTH standards for early alerts. ✔ Active learning loop — continuous model improvement using clinician feedback. Best Regards, Fahad AI/ML Engineer | Computer Vision Specialist | Medical Imaging Systems

@prakashk300

Hi! I'm an ML/Deep Learning Engineer with 4+ years of experience in Computer Vision and medical imaging AI — this project is squarely in my expertise. My relevant experience: - Built CNN-based image classification and segmentation models using TensorFlow/PyTorch and U-Net architectures - Worked with medical imaging datasets (DICOM, ultrasound), including preprocessing, augmentation, and annotation pipelines - Experience with GradCAM explainability overlays and confidence calibration for clinical AI For your AI Fetal Ultrasound Biometry Analysis System, I'll deliver: - Phase 1: Ultrasound plane detection/classification (head, abdomen, femur) using CNN on HC18, FETAL_PLANES_DB, FPUS23 - Phase 2: Semantic segmentation with U-Net for anatomical structures + ellipse fitting for HC, BPD, AC, FL, OFD, EFW measurements - Scan quality scoring, GradCAM overlays, INTERGROWTH growth chart validation, and PDF report generation - MDR/FDA 510(k) compliant audit trace engine I can deliver Phase 1 in 10 days and Phase 2 in 20 days. Let's connect to review your SRS document.

@Madnikpvtltd

Hello, I understand this is a clinically sensitive, multi-stage CV pipeline where accuracy, consistency, and auditability matter as much as model performance. I can build a two-phase system starting with robust plane classification (multi-class CNN) followed by U-Net based segmentation for anatomical structures, with post-processing for ellipse fitting and precise biometry extraction (HC, BPD, AC, FL, OFD, EFW). The pipeline will include confidence calibration, uncertainty-aware outputs, and consistency checks across measurements. I’ll structure it as a multi-task learning setup with a shared backbone and separate heads for classification, segmentation, and regression, trained on datasets like HC18 and FETAL_PLANES_DB. GradCAM-based explainability, rule-based report generation, and longitudinal growth tracking against INTERGROWTH standards will be integrated. The system will also support active learning from clinician feedback, bias monitoring, and traceable logs aligned with regulatory expectations. If you want a technically solid, extensible system that balances ML performance with clinical reliability, let’s connect.