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AI Software Triages Chest X-Rays for Pneumothorax to Reduce Time-to-Diagnosis

By MedImaging International staff writers
Posted on 01 Apr 2022
Image: Aidoc has received FDA 510(k) clearance for its triage and notification of pneumothorax on X-ray exams (Photo courtesy of Aidoc)
Image: Aidoc has received FDA 510(k) clearance for its triage and notification of pneumothorax on X-ray exams (Photo courtesy of Aidoc)

The ability to quickly identify pneumothorax is imperative as it can worsen rapidly and result in respiratory or cardiac failure. However, this is easier said than done due to the extensive imaging volumes radiologists have to continuously examine. Now, a new AI pneumothorax algorithm addresses this challenge by analyzing X-rays, flagging, and notifying physicians of the suspected findings.

Aidoc (Tel Aviv-Yafo, Israel) has received FDA 510(k) clearance for its triage and notification of pneumothorax on X-ray exams. The company’s other seven FDA-cleared solutions for flagging and communicating suspected pathologies in CT exams have now expanded to the high volume X-ray modality. Aidoc’s newly FDA-cleared solution runs on all X-ray machines, including portable ones, and is designed to analyze X-ray images. It automatically flags positive cases of pneumothorax, facilitating physicians to read X-rays in a timely manner.

Aidoc’s AI-driven solutions analyze medical images directly after the patient is scanned, suggesting prioritization of time-sensitive pathologies, as well as notifying and activating multidisciplinary teams to reduce turnaround time, shorten length of stay, and improve overall patient outcomes. Aidoc’s AI platform processes high volumes of scans and images without disrupting the physician’s workflow. The company’s existing partners who add the pneumothorax solution to their portfolio will not require any additional integration, infrastructure, or maintenance efforts.

“We’re very excited about this important milestone,” said Elad Walach, CEO of Aidoc. “This FDA clearance further validates the breadth of our AI platform, going beyond specific AI algorithms to act as a healthcare AI hub for the enterprise’s cross-specialty needs. This includes ER, ICU, outpatient centers, inpatient admissions, and the coordination of care and communication among providers. By bringing radiologists and proceduralists to the same AI platform, we enable enhanced collaboration across departments and systems to deliver patients with the right treatment at the right time.”

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Image: Researchers develop a vision-language model trained on large-scale data to generate clinically relevant findings from chest computed tomography images through visual question answering (Ms. Maiko Nagao from Meijo University, Japan)

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