Case study – Witapp

Healthcare

AI-Based Automatic Segmentation of Peritoneal Lesions

A project developed for Witapp, a highly specialized technology software house, to bring artificial intelligence into the diagnostic imaging process. The goal: to support clinical staff in identifying peritoneal lesions with a tool that combines precision, speed, and interpretive consistency.

Case study – Witapp

Healthcare

AI-Based Automatic Segmentation of Peritoneal Lesions

Witapp

Healthcare

AI-Based Automatic Segmentation of Peritoneal Lesions

Problem

01.

Manual image analysis
Non-standardized clinical assessments

In the diagnostic imaging context, identifying and segmenting peritoneal lesions requires careful analysis by clinical staff. This process can be complex, time-consuming, and subject to interpretive variability, especially with intricate images or large volumes of data. The challenge was to support healthcare professionals with a tool capable of improving precision, speed, and consistency in lesion assessment.

Solution

02.

AI model for automatic segmentation
Support for diagnostic image analysis

For Witapp, a highly specialized technology software house, we developed an AI model dedicated to the automatic segmentation of peritoneal lesions. The solution analyzes diagnostic images, identifies areas of interest, and supports clinical staff in the assessment process. The model automates a critical phase of the analysis, while keeping the healthcare professional at the center of the decision-making process.

Result

03.

Faster and more precise assessments
A more standardized clinical process

The AI solution made diagnostic image analysis more efficient, reducing the time needed to identify and segment peritoneal lesions. Clinical staff can rely on AI support capable of improving operational precision, enabling a more structured reading of images, and contributing to the standardization of the diagnostic process.

What changed

Before and after introducing
Generative AI

Before

Manual lesion segmentation

Longer analysis times

Greater variability in assessments

After

AI-supported segmentation

Faster and more structured assessments

A more standardized analysis process

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AI-Based Automatic Segmentation of Peritoneal Lesions

AI-Based Automatic Segmentation of Peritoneal Lesions