Telepathology for Patient Care: What Am I Getting Myself Into?
Overview
Pathology
Affiliations
The vast advancements in telecommunications and converting medical information to a digital format have increased the number of applications within telemedicine. Telepathology, in simplest terms, is the practice of formally rendering a pathologic diagnosis based upon examination of an image rather than of a glass slide through traditional microscopy. The use of telepathology for clinical patient care has so far been limited to relatively few large academic institutions. Although a number of challenges remain, there is increasing demand for the use of information technology in pathology as a whole owing to the expansion of health care networks and the opportunity to enhance the quality of service delivered to patients. The software used to acquire, display, and manage digital images for clinical patient care may be subject to national and federal regulations just as is any other electronic information system. Despite the barriers, telepathology systems possess the capability to help manage pathology cases on a global scale, improve laboratory workload distribution, increase standardization of practice and enable new classes of ancillary studies to facilitate diagnosis and education even in the most remote parts of the earth.
Siggaard L, Jacobsen H, Hougaard D, Hogsbro M Front Digit Health. 2023; 5:1182421.
PMID: 37363275 PMC: 10285396. DOI: 10.3389/fdgth.2023.1182421.
Review of Systematic Reviews in the Field of Telemedicine.
Goharinejad S, Hajesmaeel-Gohari S, Jannati N, Goharinejad S, Bahaadinbeigy K Med J Islam Repub Iran. 2022; 35:184.
PMID: 36042824 PMC: 9391764. DOI: 10.47176/mjiri.35.184.
A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.
Wang A, Zhang Q, Han Y, Megason S, Hormoz S, Mosaliganti K Sci Rep. 2022; 12(1):342.
PMID: 35013443 PMC: 8748745. DOI: 10.1038/s41598-021-04048-3.
Yellowlees P, Burke Parish M, Gonzalez A, Chan S, Hilty D, Yoo B J Med Internet Res. 2021; 23(7):e24047.
PMID: 33993104 PMC: 8335606. DOI: 10.2196/24047.
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images.
Mahmood F, Borders D, Chen R, McKay G, Salimian K, Baras A IEEE Trans Med Imaging. 2019; 39(11):3257-3267.
PMID: 31283474 PMC: 8588951. DOI: 10.1109/TMI.2019.2927182.