abstract

• Infrared thermography is a non-invasive technique that records temperature patterns of the body which can be used as an adjunct method for the detection of diverse medical pathologies. One limitation of medical thermology is incomplete coherence to a standardized interpretation procedure which can lead to disparity of results given by different interpreters. Artificial Intelligence and deep learning models have been used for pattern recognition and classification of objects and have been adopted elsewhere as an adjunct methodology in medical imaging diagnosis. To perform deep learning algorithms a large database of infrared thermographic images must be put together. These images likely will come from different clinics, use different imaging systems, and they are saved in different proprietary formats depending on the imager used. To circumvent these difficulties a standard format for saving medical thermology studies must be implemented. In this talk the efforts of the American Academy of Thermology (AAT) are presented to standardize a Digital Imaging and Communication in Medicine (DICOM) file format for medical thermology. Also, efforts to implement a Picture Archiving and Communication System (PACS) Software to handle large number of images for thermography big data applications will be presented. Artificial Intelligence applications towards adjunct detection of breast pathologies using infrared thermography will be discussed. © 2022 SPIE.

authors

• González Contreras Francisco Javier
• Schwartz R.G.

publication date

• 2022-01-01

published in

• Proceedings of SPIE - The International Society for Optical Engineering  Proceedings

keywords

• deep learning; DiCOM; machine learning; Medical thermography; PACS Application programs; Computer aided diagnosis; Deep learning; Learning algorithms; Learning systems; Medical applications; Medical imaging; Pathology; Pattern recognition; Picture archiving and communication systems; Deep learning; DiCOM; Digital imaging and communication in medicines; File formats; Machine-learning; Medical thermography; Noninvasive technique; Picture archiving and communications systems; Temperature patterns; Thermology; Thermography (imaging)

Digital Object Identifier (DOI)

• 10.1117/12.2623624

PubMed ID

start page

end page

volume

• 12107

issue