ReviewJournal of cytology
A Critical Review of Deep Learning Technique and Its Applications in Clinical Cytology.
Review in Journal of cytology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) is an emerging area of artificial intelligence and has the immense potential to reshape the field of cytology. In last few decades, there are many significant progresses in the digital pathology that includes significant increase of the computational power, data storing facilities, and whole slide imaging system. These changes have facilitated the applications of DL. DL can be used in cancer screening, disease identification and classification, predicting biomarkers, prognostic assessment, molecular data interpretation, and precision medicine. There are many obstacles to implement DL. Data collection, storage, security, computational power, and medico-legal issue are the major hurdles. The proper guidelines, finance, organization, and collaboration with multiple disciplines are needed to implement DL. Till date, the majority of the cytologists are not totally aware of the potential of DL. In this review, the overview of the DL system along with its potential clinical applications has been discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.