Generic filters
Filter by input type
Select all
News
Pages
Events
Filter by category
Select all
AI ANALYTICS
Mobile Apps and Internet of Things
Advancement of science
big data
Connected communities
Coronavirus
Courses and training
DIAGNOSIS
Initial Editorial
Editorials
A world in the cloud
Events
Infographics
Artificial Intelligence and Science
IoT Apps
News
Digital platforms
Social networks
Review of scientific publications
Course Summary
Synopsis of essay
Overview of reference frames
Synopsis of recent publications
Use of Digital Platforms
Generic filters
Filter by input type
Select all
News
Pages
Events
Filter by category
Select all
AI ANALYTICS
Mobile Apps and Internet of Things
Advancement of science
big data
Connected communities
Coronavirus
Courses and training
DIAGNOSIS
Initial Editorial
Editorials
A world in the cloud
Events
Infographics
Artificial Intelligence and Science
IoT Apps
News
Digital platforms
Social networks
Review of scientific publications
Course Summary
Synopsis of essay
Overview of reference frames
Synopsis of recent publications
Use of Digital Platforms
They develop an Artificial Intelligence tool that detects lost information in radiological studies

Through the IBM Watson Health Digital Health tool, a white paper on lost information in radiology using Artificial Intelligence (AI) was published.

Missing information or findings is a relatively common and well-known problem in radiological studies. This problem is caused by various factors, such as reader fatigue, even a distraction by the professional in charge.

For example, the detection of lung nodules through radiological images can be very complicated, since cancer detection programs are used for this, which can overload the system. In this sense, this can lead to erroneous results and even lawsuits against specialists for malpractice.

The AI tool was developed through image and text algorithms that jointly analyze computed tomography (CT) studies and radiology reports.

“The algorithm is designed to detect any non-calcified pulmonary nodule with a diameter greater than 6 mm. The text AI algorithm determines if any pulmonary nodules were mentioned in the associated clinical report. If a pulmonary nodule is identified on the imaging study, but is not mentioned as present in the text of the radiology report, the study is identified as having a missing potential pulmonary nodule,” the study explains.

The research involved CT scans from hospitals in the UK and the US. Studies that were identified by the AI as potentially failing to detect lung nodules were reviewed by the American Board of Radiology (ABR). In this way, the imaging studies with nodules detected and the radiological studies without reported nodules were reviewed by specialists.

“This study demonstrates the utility of our AI tool in identifying missed lung nodules in a large and diverse set of CT scans of the chest and abdomen that reflect a real clinical setting. Of a total of 32,134 studies, 100 true missed nodules were identified with only 315 false positives”, concludes the study, which was presented during the AIMed summit.

You can consult the full study at the following link: https://ai-med.io/wp-content/uploads/2022/05/IBM_Watson_Health_Imaging_AI_Research_Missed_Lung_Nodules_White_Paper_1_1.pdf

Last Tweets

Digital Health Events

2022 November

Semana 1

Mon 31
tue 1
wed 2
Thu 3
Fri 4
Sat 5
Sun 6
Mon 7
tue 8
wed 9
Thu 10
Fri 11
Sat 12
Sun 13
Mon 14
tue 15
wed 16
Thu 17
Fri 18
Sat 19
Sun 20
Mon 21
tue 22
wed 23
Thu 24
Fri 25
Sat 26
Sun 27
Mon 28
tue 29
wed 30
Thu 1
Fri 2
Sat 3
Sun 4
  • No Events

  • No Events

  • No Events

  • No Events

  • No Events

Share

Digital Health in the world

  • — Science Brief: Omicron (B.1.1.529) Variant/CDC updates
    See more
  • —Coronavirus resource center/Johns Hopkins
    See more
  • — Epidemiological tracing of COVID-19 contacts / Johns Hopkins Course
    See more
  • — SARS-CoV-2 infection behavior / FCS calculator
    See more
  • — Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic/ Article The Lancet
    See more
  • —Genomic Epidemiology Tracker/GISAID
    See more
  • — Mexican Genomic Surveillance Consortium
    See more
mistake: This content is protected...