🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across multiple categories, with the only notable concern being the potential for shell command execution. However, this does not strongly indicate malicious intent without additional context.

Moderate shell risk due to subprocess execution
No network calls, obfuscation, or credential harvesting detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: Subprocess execution can be legitimate but may indicate potential for executing arbitrary commands, warranting further investigation into the package's purpose and usage.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

self.close() subprocess.run(cmd) def main() -> int: app = QApplication(sys.argv)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: i3m.upv.es>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository EdAlita/ChameleonIQ appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Edwing Ulin-Briseno" 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 ChameleonIQ

Create a fully-functional mini-application using the Python package 'ChameleonIQ' for analyzing medical imaging quality according to the NEMA NU 2-2018 standard. This application should serve as a user-friendly tool for radiologists and imaging technicians to quickly assess and improve image quality in their diagnostic images. Here’s a detailed step-by-step guide on how to build this application:

1. **Setup**: Begin by installing the necessary packages including 'ChameleonIQ'. Ensure your development environment is set up with Python and all dependencies installed.

2. **User Interface**: Design a simple yet effective graphical user interface (GUI) using a library like Tkinter or PyQt. The GUI should allow users to easily upload DICOM files for analysis.

3. **Image Upload & Preprocessing**: Implement functionality within the application to allow users to select and upload DICOM files from their local system. Use ChameleonIQ to preprocess these images if needed, ensuring they meet the standards required for analysis.

4. **Analysis**: Utilize ChameleonIQ's core functionalities to perform NEMA NU 2-2018 Image Quality analysis on the uploaded images. This includes evaluating parameters such as spatial resolution, noise, contrast, and more.

5. **Results Presentation**: Display the results of the analysis in a clear, understandable format through the GUI. Consider adding visual aids such as graphs or charts to better illustrate findings.

6. **Report Generation**: Allow users to generate detailed reports based on the analysis results. These reports should be exportable as PDFs or other common document formats for easy sharing and record-keeping.

7. **Enhancements**: Consider adding additional features such as automatic detection of problematic areas in the images, suggestions for improving image quality, and comparison tools to track improvements over time.

Remember to thoroughly test the application with various DICOM files to ensure accuracy and reliability of the analysis provided by ChameleonIQ.