Mohammed A.

Key Skills

Software

Other

Internal/Proprietary Tooling

Top Subject Matter

Orthopaedic pelvic surgery

bone CT segmentation (pelvis/hip)

Top Data Types

Text

Medical Dicom

Document

Top Task Types

Segmentation

Data Collection

Classification

Text Summarization

Freelancer Overview

Orthopaedic surgeon and medical AI researcher with a doctoral affiliation at Uniklinik RWTH Aachen, Germany. I specialize in bone CT segmentation — manual, automated, and AI-driven — with hands-on experience building complete annotation and validation pipelines using 3D Slicer and DICOM-native workflows. I own a proprietary dataset of ~4,000 pelvic CT scans, categorized by diagnosis, and have built and trained a team for structured medical data collection and quality control. I am the sole developer of OrthoInsighter Pelvis, an automated pelvic CT segmentation tool validated across 81 anatomical segments, and the founder of OrthoNova — currently building the first domain-specific LLM for hip and pelvis surgery, trained on 82,197 surgeon-curated records. My combination of clinical expertise, AI development experience, and active dataset ownership makes me uniquely positioned for medical imaging annotation, AI training, and dataset curation tasks.

Labeling Experience

Doctoral Researcher — Pelvic & Arthroplasty Unit (medical AI & imaging: CT segmentation, DICOM annotation pipeline management)

Segmentation

Built and trained annotation and segmentation workflows for pelvic and proximal femoral bone CT datasets to support medical AI development. Performed manual, automated, and AI-driven bone CT segmentation across clinically relevant regions and landmarks. Managed the DICOM annotation pipeline to generate validated labels for downstream training and evaluation. • Labeled/segmented ~4,000 legally acquired pelvic CT scans categorized by diagnosis • Developed and used 3D Slicer–based segmentation workflows and custom modules • Conducted annotation QA and validation to ensure label correctness • Supported research validation for anatomy-based subsegmentation across pelvic regions

2024 - Present

Education

Work History