Scaida – ThoughtsWin Systems

AI-Powered diagnostic assistant

Scaida is an innovative AI-powered platform designed to transform how medical imaging diagnostics are handled. It automates the analysis of radiology images, enabling faster, more accurate detection of anomalies like fractures, hemorrhages, and tumors.

Scaida seamlessly integrates with existing HIS/PACS systems and offers cloud-based accessibility, allowing healthcare professionals to make critical diagnostic decisions from anywhere. By reducing human error and improving diagnostic efficiency, Scaida enhances patient care while streamlining radiology workflows for clinics and hospitals.

Problem

Healthcare institutions face challenges in managing, interpreting, and diagnosing medical images due to high volumes and variability in image quality. Radiologists are often overwhelmed by the time-consuming nature of analysis, leading to delays and potential human errors.

Solution

Scaida is an innovative AI-Powered diagnostic assistant designed specifically for radiologists. Scaida platform offers a range of features to help radiologists prioritize cases, generate standardized reports, and reduce the burden of diagnostics.

Benefits

Improves Accuracy

Scaida utilizes cutting-edge AI algorithms to automatically analyze and interpret medical images, significantly reducing manual effort and improving accuracy.

Enhances Collaboration

Scaida provides a collaborative environment for radiologists to easily consult with peers, share insights, and discuss complex cases, fostering a culture of knowledge-sharing and continuous improvement.

Self Learning and Evolving

Scaida continuously learns from new data and radiologist’s feedback, enabling the AI to evolve and improve over time, staying up-to-date with the latest advancements in medical imaging.

Enables Automation

Scaida streamlines radiology workflow by automatically populating standardized templates from an extensive library for radiologists. This enhancement in automation leads to increased efficiency and consistency in reporting, allowing radiologists to focus on more complex diagnostic tasks.

Provides Decision Support

Scaida aids in identifying scans that may warrant prioritization, yet the ultimate decision to prioritize remains with the radiologist. This ensures that the expertise of radiologists is combined with the power of AI, resulting in improved diagnostic efficiency without compromising professional judgment.

Use Scaida for

Triage

Radiology

Reporting

Brain CT

Workflow & Features

Enhanced Collaboration For Complex Cases Improving Patient Outcomes

Intelligent, Standardized Templates To Improve Diagnostic Reporting

Improved Workflow And Decision Support

Seamless Integration For Minimal Disruptions In Daily Routines

Reduced Time To Identify And Prioritize Critical Cases

Leverages Cloud to Reduce Infrastructure Challenges

Scaida Flow is a cloud-based diagnostic platform for radiologists, enabling case management, remote access, and efficient report generation. It offers migration services to transition from on-site storage to the cloud, preserving clinical history. Unlike Scaida, it does not include AI modules but helps radiologists saving time using templates for auto-populated reports, improving productivity.

Scaida DetectCT is a cloud-based, AI-driven module that integrates seamlessly with radiology workflows, offering scalability and accessibility. Powered by advanced machine learning, it streamlines diagnostic processes by rapidly detecting abnormalities in various anatomical regions like the head, neck, chest, and pelvis. Its flexibility and precision enhance diagnostic accuracy, improving patient care.

Scaida BrainCT is an advanced AI-driven module designed for neuroradiologists, seamlessly integrating into existing workflows with cloud-based flexibility and scalability. It enhances brain CT scan diagnostics by using neural networks to detect anomalies such as traumatic injuries, hemorrhages, infections, tumors, and degenerative conditions. By offering detailed semantic segmentation models, BrainCT acts as a second layer of analysis, reducing human error and streamlining diagnostics.