🤖 AI Analysis

Final verdict: SAFE

The package has a low risk score due to minimal security concerns and no evidence of malicious activity or supply-chain attacks.

Low risk scores across all categories.
Concerning metadata with non-secure links.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function properly.
Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The presence of non-secure links is concerning, but no other significant red flags are present.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: tum.de

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:8000
Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0

✓ Git Repository History

Repository Hendrik-code/spineps appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Hendrik Möller" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with SPINEPS

Create a medical imaging application using Python that leverages the SPINEPS framework for spinal MRI segmentation. Your application should allow users to upload their own MRI scans and receive segmented images of the entire spine as output. Here are the key steps and features your app should include:

1. **User Interface**: Develop a simple web-based UI where users can upload MRI scan files. Ensure the interface is user-friendly and provides feedback on file upload progress.
2. **MRI File Handling**: Implement functionality to handle DICOM files commonly used in medical imaging. Use libraries like pydicom to read and preprocess these files.
3. **Segmentation Engine**: Utilize SPINEPS for the segmentation process. Integrate SPINEPS into your application to perform automatic whole spine segmentation from uploaded MRI scans.
4. **Visualization**: Provide a visualization tool within the app to display the original MRI scan and the segmented spine images side-by-side for easy comparison.
5. **Results Export**: Allow users to download the segmented images in a common format such as PNG or JPEG, alongside a report summarizing the segmentation results.
6. **Error Handling & Feedback**: Implement robust error handling to manage issues such as unsupported file formats or failed segmentation processes. Provide clear messages to guide users through any encountered problems.
7. **Security Measures**: Since medical data is sensitive, ensure all data transmission and storage within your app are secured using HTTPS and other appropriate measures.

This project will not only demonstrate the power of SPINEPS in real-world applications but also provide a valuable tool for medical professionals working with spinal MRI scans.