Enlitic, Laitek ink data migration support agreement with GEHC

AuntMinnie.com staff writers

Feb 18, 2025

Enlitic, a firm specializing in AI-driven healthcare imaging software, and its subsidiary Laitek have signed an agreement with GE HealthCare (GEHC) to provide its AI-based Ensight Suite to support data migration across GEHC's delivery networks.

As a collaborator in GEHC's software as a service (SaaS) and cloud transformation program, Enlitic will embed its proprietary AI automation and data intelligence into migrations tools for GEHC's cloud and on-premises transitions.

Under the collaboration, the firms will provide a secure and scalable framework for the transition of customers to GEHC's newest Enterprise Imaging and PACS solutions such as Datalogue and True PACS, while maintaining the quality of the imaging data, according to the two firms.

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Full-field digital screening mammograms in participants recalled by the consensus discussion after being flagged by artificial intelligence (AI) computer-aided detection (CAD) but not by either of the two radiologists. (A) Mammogram with AI CAD score of 73 for a 55-year-old woman diagnosed with grade 3 in situ cancer and T1 (9-mm) invasive cancer in the left breast and lymph node metastasis. (B) Mammogram with AI CAD score of 54 for a 49-year-old woman diagnosed with grade 2 in situ cancer and T1 (6-mm) invasive cancer in the right breast and without lymph node metastasis. All images and caption courtesy of the RSNA.

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MRI scans show two areas of clinically significant prostate cancer in a 65-year-old man with a prostate-specific antigen level of 8.23 ng/mL. Images are (A, E) axial T2-weighted images, (B, F) axial diffusion-weighted images obtained with a b value of 1500 sec/mm2, (C, G) axial apparent diffusion coefficient images, and (D, H) lesion masks generated by the deep learning algorithm (DLA) on T2-weighted images. The DLA detected a lesion in the right posteromedial peripheral zone of the apex (D), which was assigned as Prostate Imaging Reporting and Data System (PI-RADS) 4. A radiologist also detected the lesion and assigned it PI-RADS 4 (arrow in A-C). A lesion in the right anterior transition zone (H) was detected by the DLA, which was assigned PI-RADS 4. The radiologist did not detect the lesion (arrow in E-G). Both lesions were confirmed as prostate cancer with Gleason grade group 2. Caption and image courtesy of RSNA.

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Full-field digital screening mammograms in participants recalled by the consensus discussion after being flagged by artificial intelligence (AI) computer-aided detection (CAD) but not by either of the two radiologists. (A) Mammogram with AI CAD score of 73 for a 55-year-old woman diagnosed with grade 3 in situ cancer and T1 (9-mm) invasive cancer in the left breast and lymph node metastasis. (B) Mammogram with AI CAD score of 54 for a 49-year-old woman diagnosed with grade 2 in situ cancer and T1 (6-mm) invasive cancer in the right breast and without lymph node metastasis. All images and caption courtesy of the RSNA.

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MRI scans show two areas of clinically significant prostate cancer in a 65-year-old man with a prostate-specific antigen level of 8.23 ng/mL. Images are (A, E) axial T2-weighted images, (B, F) axial diffusion-weighted images obtained with a b value of 1500 sec/mm2, (C, G) axial apparent diffusion coefficient images, and (D, H) lesion masks generated by the deep learning algorithm (DLA) on T2-weighted images. The DLA detected a lesion in the right posteromedial peripheral zone of the apex (D), which was assigned as Prostate Imaging Reporting and Data System (PI-RADS) 4. A radiologist also detected the lesion and assigned it PI-RADS 4 (arrow in A-C). A lesion in the right anterior transition zone (H) was detected by the DLA, which was assigned PI-RADS 4. The radiologist did not detect the lesion (arrow in E-G). Both lesions were confirmed as prostate cancer with Gleason grade group 2. Caption and image courtesy of RSNA.

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