Imagen’s telespecialist network facilitates a single, comprehensive visit to reduce unnecessary external referrals.
Complete testing, results, and consultation in a single patient visit.
Avoid unnecessary, high-cost specialist visits, emergency department utilization, and hospital admissions.
Oversee the patient journey, from first contact to treatment, all without chasing paperwork or going between EMR’s.
Offer an incredible patient experience, with timely testing & results, without multiple appointments and unnecessary travel.
Give patients confidence by removing the toll of waiting for results and follow-up appointments.
Point-of-care access via mobile or desktop devices to specialists for immediate, coordinated patient care.
HIPAA compliant virtual specialist patient visits ensuring privacy.
Fully secure, integrated service platform automates specialist access to pertinent patient data, imaging and results.
Specialist encounter notes and results are integrated directly into your EMR.
This work shows how Imagen’s FDA-cleared software for detecting fractures (FractureDetect) in musculoskeletal radiographs improves clinicians’ accuracy at diagnosing fractures. Both clinicians with extensive training in musculoskeletal imaging and clinicians with limited training were significantly more accurate when assisted by our deep-learning system.
In this work, Imagen shows the effectiveness of Chest-CAD, our FDA-cleared software that analyzes chest radiograph studies using artificial intelligence. Our software highlighted a suspicious part of an X-ray missed through unaided interpretation, thus showing the promise of the additional information provided by our artificial intelligence software.
Imagen developed a deep-learning system for detecting fractures across the musculoskeletal system. In a multi-site study, the deep-learning system accurately identified fractures throughout the adult musculoskeletal system. The results were used, in part, to support FDA clearance of Imagen’s software (FractureDetect) to assist clinicians in detecting fractures for a selected subset of the anatomic regions reported in this paper.
Imagen developed a deep neural network to detect and localize fractures in wrist radiographs. A controlled experiment showed that the average clinician had a 47% relative reduction in their misinterpretation rate using the deep-learning model. This significant improvement in diagnostic accuracy shows that deep-learning methods can substantially enhance patient care.