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Development and commercialisation of AI platform to disrupt the ultrasound guided invasive procedures training for junior doctors and medical students

Development and commercialisation of AI platform to disrupt the ultrasound guided invasive procedures training for junior doctors and medical students
人工智能平台的开发和商业化将颠覆初级医生和医学生的超声引导侵入性手术培训
批准号:
10032495
负责人:
金额:
$48.88万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2022
资助国家:
英国
项目状态:
已结题
起止时间:
2022 至 --

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英文摘要
Ultrasound assistance improves patient conditions management and serves as an invaluable tool for numerous clinical specialties. However, its successful application depends in on the clinician's skills and is operator-dependent and therein lays one of the greatest challenges faced by clinicians in widely adopting and undertaking bedside ultrasound procedures.Today, trainee doctors ultrasound assisted procedural training is provided in the NHS operated training centres through approved instructors. A typical training day-session (costing £100 to £275 per student) surrounds instructor providing guidelines and synthetic subject or patient being scanned using an ultrasound knob with off-line ultrasound images appearing on the display screen. Because of instructor and patient's limited-time availability, students gain maximum of 30min hands-on time per session. Key limitations of existing training approaches include;* course's inherent inability to provide training on limited cases,* instructor and trainee doctors' limited availability on specific times/locations,* non-representation of normal anatomy in mannequins,* limited use as needle insertions distorts images obtained by ultrasoundTo rectify the shortcomings of existing training solutions, through this 18 months project, Ashmount Services and De Montfort University Leicester (DMU), are developing world's first Augmented Reality (AR) and Artificial Intelligence (AI) powered ultrasound training platform 'SIMS' for trainee doctors.SIMS will provide junior doctors the freedom to undertake training as per their availability, reduce human errors by at-least 99% and expand ultrasound experience using specific cases from the catalogue. It will enable healthcare providers to uniformly train and assess large numbers of doctors in an automated way.
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