Paus-Ev AI: Hardware Architecture, Medical-AI Design, and Clinical Translation of a Non-Invasive Pho

Photoacoustic Laser-Ultrasound Medical AI presents a research and engineering framework for a non-invasive screening system for gastroesophageal varices in adults with portal hypertension. The proposed PAUS-EV AI platform combines multispectral photoacoustic imaging, pulse-echo ultrasound, Doppler flow analysis, three-dimensional reconstruction, embedded safety control, and medical AI.The book covers clinical need, anatomical access challenges, optical and acoustic feasibility, probe design, certified laser integration, ultrasound electronics, FPGA-based acquisition, signal processing, AI quality gating, risk estimation, cybersecurity, verification, regulatory planning, and clinical validation.A central design principle is that AI must first confirm adequate anatomy, target depth, probe coupling, motion control, signal quality, channel integrity, and safety metadata before producing a risk estimate. When an acquisition is inadequate, the system must return an indeterminate or non-diagnostic result rather than a misleading negative output.Written for biomedical engineers, clinicians, developers, researchers, medical-AI specialists, regulatory experts, and students, this book provides a practical roadmap from simulation and prototype development to multicentre validation and clinical translation.The system is intended for screening and clinical decision support, not as a replacement for emergency endoscopy, endoscopic treatment, or clinically indicated surveillance.