Evidence map›Paper›PMID 36620183›Full record

ReviewFrontiers in digital health2022

Expanding the horizon for breast cancer screening in India through artificial intelligent technologies -A mini-review.

Sudip Bhattacharya, Saurabh Varshney, Petra Heidler, Shailesh K Tripathi

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  3. Review
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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

4 authors.

Sudip BhattacharyaDepartment of Community and Family Medicine, All India Institute of Medical Sciences, Deoghar, India.
Saurabh VarshneyDepartment of ENT (Otorhinolaryngology), All India Institute of Medical Sciences, Deoghar, India.
Petra HeidlerDepartment for Economy and Health, University for Continuing Education Krems, Krems an der Donau, Austria.
Shailesh K TripathiRajendra Institute of Medical Sciences, Ranchi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Breast cancer is one of the most common cancer among Indian women, with an incidence of 25.8 per 100,000 women according to the Ministry of Health and Family Welfare. Late detection is responsible for poor quality of life (QOL), and it is the leading cause of death. In metropolitan regions, one in every 22 women will have breast cancer over their lifetime; but in rural areas, one in every 60 women will develop breast cancer as per estimates. Aim and objective: This paper aims to describe the various AI based breast screening technologies which are used in breast cancer screening in India. Methodology: The literature search was done using "Pub Med," "Google scholar," and "Scopus" databases for the key terms "technology," "cancer research," "artificial intelligence," "mammography", "breast cancer," "cancer," and/or "neoplasia in breast." All the relevant articles were included to support this mini review. Results: We found that emerging artificial intelligent technologies namely "Niramai", "iBreastExam," "MammoAssist" are emerging as an hope for early detection by screening in resource poor settings, in turn, which can improve the QOL among breast cancer patients.

Indexed as

artificial intelligencebreast carcinomacancer diagnosticscancer prognosiscomputational intelligence

Identifiers

PMID36620183
PMCPMC9822715

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.