Evidence map›Paper›PMID 31496588›Full record

ArticleIndian journal of surgical oncology2019

Utility of WhatsApp as a Tool for Tele-oncopathology for Oral Lesions.

Neha Garg, Nadeem Tanveer, Jyotsana Harit Gaur

Abstract read
In one paragraph

Article in Indian journal of surgical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Neha GargDepartment of Pathology, University College of Medical Sciences, Dilshad Garden, Delhi, 110095 India.
Nadeem TanveerDepartment of Pathology, University College of Medical Sciences, Dilshad Garden, Delhi, 110095 India.ORCID 0000-0002-7289-9225
Jyotsana Harit GaurDepartment of Pathology, University College of Medical Sciences, Dilshad Garden, Delhi, 110095 India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional telepathology techniques like whole slide imaging and robotic microscopy are too expensive to be used in developing countries. With the advent of smartphones with high-resolution cameras and faster internet services, it is now possible to send good-quality images to pathologist for diagnosis. To study the utility of WhatsApp image transfer in the histopathological diagnosis of common oral malignant and benign lesions. A total of 100 cases of oral biopsy were included in the study. These comprised of 58 cases of squamous cell carcinoma, 33 cases of oral leukoplakia, 3 cases of oral lichen planus and 6 cases of moderate to severe dysplasia. The conventional microscopy diagnosis made by glass slide viewing was taken as the gold standard and the concordance of the WhatsApp diagnosis with the gold standard was calculated for each category of diagnosis and also for all the 100 cases. The concordance rate for squamous cell carcinoma was 96.6%(56/58). For leukoplakia, there was 100%(33/33) concordance between the WhatsApp diagnosis and conventional microscopy. The concordance rates for dysplasia (4/6) and oral lichen planus (2/3) were 66.7% each. Hence, for all the categories taken together, the overall concordance rate was 95%. A concordance rate of 95% is very encouraging; however, for routine diagnostic use in telepathology, there is still a long way to go. With the availability of better smartphone cameras and faster internet, it will be possible to send better quality images in the future. The pathologists will also learn where to draw the line and ask for glass slides for final diagnosis.

Indexed as

Oral biopsySmartphone assisted telepathologyTele-oncopathology

Identifiers

PMID31496588
PMCPMC6708029

What OpenQuestion holds

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Registered trials

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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.