You are a professional scientific communication designer specializing in IEEE, Springer, biomedical engineering, medical imaging, and healthcare AI conference posters. Design a publication-quality academic research poster that looks professionally created by a university research laboratory or medical AI research group. The poster must not look AI-generated, promotional, or template-based. It must resemble a human-designed conference poster suitable for submission through a company to an international research conference. Poster specifications Create a single A0 portrait poster (841 × 1189 mm) at print-ready quality (300 DPI equivalent). Use a traditional three-column academic conference layout with a clear left-to-right and top-to-bottom reading flow. Use a white background, dark navy blue section headers, black body text, and subtle gray dividers. Use only two fonts: Helvetica or Arial for headings and body text. Maintain a clean academic aesthetic with strong alignment, consistent spacing, and generous white space. Target composition: - 40–45% graphics and diagrams - 20–25% text - 30–35% white space The poster must be readable from 1–2 meters away. Typography hierarchy - Title: 88–96 pt - Authors: 34–38 pt - Affiliations: 24–28 pt - Section headings: 42–48 pt - Body text: 24–28 pt - Figure captions: 20–22 pt - References: 18–20 pt Do not use decorative fonts, gradients, shadows, glowing effects, 3D icons, or marketing-style graphics. Title block Title: Stroke Detection Using AI and IoT-Based Monitoring: A Multimodal CT Imaging and Wearable Sensor Framework Authors: Nikhil Savita, Jonathan Raposo, Sakshi Kamble, Yashraj Dilip Naik, Louella M. Colaco, Meghana Pai Kane Affiliation: Department of Computer Engineering, Padre Conceição College of Engineering, Verna, Goa, India Prepared for conference poster presentation. Include a clean institutional header with a company logo placeholder on the top right. Column 1: Background, problem statement, objective Background Use concise bullet points. - Stroke is a leading cause of mortality and long-term disability. - Early detection is critical because brain tissue damage progresses rapidly. - Hospital CT diagnosis is accurate but episodic. - Wearable monitoring is continuous but prone to false positives. - Existing systems do not integrate imaging diagnosis with continuous physiological monitoring. Research gap State prominently: No existing platform combines hospital-grade CT image intelligence with continuous wearable physiological monitoring for real-time stroke risk assessment. Objective Develop and evaluate a dual-layer AI-IoT framework that combines CT image classification with continuous wearable monitoring to improve stroke detection reliability and reduce false-positive alerts. Add a small visual callout box titled Clinical Motivation with an icon of brain imaging and wearable monitoring. Column
You are a professional scientific communication designer specializing in IEEE, Springer, biomedical engineering, medical imaging, and healthcare AI conference posters. Design a publication-quality academic research poster that looks professionally created by a university research laboratory or medical AI research group. The poster must not look AI-generated, promotional, or template-based. It must resemble a human-designed conference poster suitable for submission through a company to an international research conference.
Poster specifications
Create a single A0 portrait poster (841 × 1189 mm) at print-ready quality (300 DPI equivalent). Use a traditional three-column academic conference layout with a clear left-to-right and top-to-bottom reading flow. Use a white background, dark navy blue section headers, black body text, and subtle gray dividers. Use only two fonts: Helvetica or Arial for headings and body text. Maintain a clean academic aesthetic with strong alignment, consistent spacing, and generous white space.
Target composition:
- 40–45% graphics and diagrams
- 20–25% text
- 30–35% white space
The poster must be readable from 1–2 meters away.
Typography hierarchy
- Title: 88–96 pt
- Authors: 34–38 pt
- Affiliations: 24–28 pt
- Section headings: 42–48 pt
- Body text: 24–28 pt
- Figure captions: 20–22 pt
- References: 18–20 pt
Do not use decorative fonts, gradients, shadows, glowing effects, 3D icons, or marketing-style graphics.
Title block
Title:
Stroke Detection Using AI and IoT-Based Monitoring: A Multimodal CT Imaging and Wearable Sensor Framework
Authors:
Nikhil Savita, Jonathan Raposo, Sakshi Kamble, Yashraj Dilip Naik, Louella M. Colaco, Meghana Pai Kane
Affiliation:
Department of Computer Engineering, Padre Conceição College of Engineering, Verna, Goa, India
Prepared for conference poster presentation.
Include a clean institutional header with a company logo placeholder on the top right.
Column 1: Background, problem statement, objective
Background
Use concise bullet points.
- Stroke is a leading cause of mortality and long-term disability.
- Early detection is critical because brain tissue damage progresses rapidly.
- Hospital CT diagnosis is accurate but episodic.
- Wearable monitoring is continuous but prone to false positives.
- Existing systems do not integrate imaging diagnosis with continuous physiological monitoring.
Research gap
State prominently:
No existing platform combines hospital-grade CT image intelligence with continuous wearable physiological monitoring for real-time stroke risk assessment.
Objective
Develop and evaluate a dual-layer AI-IoT framework that combines CT image classification with continuous wearable monitoring to improve stroke detection reliability and reduce false-positive alerts.
Add a small visual callout box titled Clinical Motivation with an icon of brain imaging and wearable monitoring.
Column
Created using ChatSlide
This presentation addresses the pressing clinical need for earlier stroke detection through an innovative dual-layer AI-IoT framework. By integrating CT intelligence with wearable monitoring, we aim to enhance reliability and minimize false alerts. Our objective is to evaluate the performance of this multimodal approach in real-world clinical settings, ultimately improving patient outcomes and advancing stroke management.