Image Understanding Research and Its Application to Cartography and Computer-Based Analysis of Aerial Imagery

Fischler, Martin A.

doi:10.21236/ada221891

Cited by 1 publication

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there are resources that can help with this issue, such as decision support software, virtual consult platforms, and imaging ordering recommendations, ML can offer a more thorough, evidence-based resource [ 30 , 31 ]. In addition, a significant corpus of research is also starting to exploit multimodal, multiscale data fusion for biomedical imaging and ML applications [ 32 ]. In order to recommend an appropriate patient-specific imaging examination tailored to the clinical question that must be answered, ML algorithms can incorporate multiple sources of information from a patient’s medical records, such as symptoms, laboratory values, physical examination findings, and previous imaging results [ 33 ].…”

Section: Imaging Appropriateness and Protocolingmentioning

confidence: 99%

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Cui

Zhu

Duan

et al. 2022

IJERPH

View full text Add to dashboard Cite

Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.

show abstract