Wednesday December 11, 2019
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Community Health Centres deprived of specialists

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By NewsGram Staff Writer

New Delhi: An IndiaSpend analysis revealed that the Indian public health systems are in dire need of attention and investment, especially in the rural areas. With a shortage of 83 percent of medical specialists, stated by the Rural Health Statistics, 2015 and released by the Ministry of Health and Family Welfare, Community Health Centres if ignored would leave a lot of people helpless. Arunachal Pradesh, Kerala, Manipur, Meghalaya and Tamil Nadu are some of the states that have no surgeons in their CHCs. Then there is a 76-percent shortage of obstetricians and gynaecologists in CHCs nationwide.

AIIMS_slumAn ideal CHC is a 30-bedded hospital which is meant to provide specialist care in medicine, obstetrics and gynaecology, surgery, paediatrics, dental and Ayurveda, yoga and naturopathy, unani, siddha and homoeopathy (AYUSH) according to the Indian Public Health Standards prescribed by the Ministry of Health and Family Welfare in 2012. The CHCs constitute the secondary level of health care serving roughly 80,000 people in tribal, hill or desert areas and 120,000 on the plains.

In rural India, 58 percent of hospitalised treatment was carried out in private hospitals, while in urban India the figure was 68 percent, according to the Key Indicators of Social Consumption on Health 2014 survey, carried out by National Sample Survey Office (NSSO). While infant mortality rate declined from 83 per 1,000 live births in 1990 to 44 per 1,000 live births in 2011, and maternal mortality ratio reduced from 570 per 100,000 live births in 1990 to 212 in 2007-2009, both indicators remain high compared to other BRICS countries like Brazil, Russia, China and South Africa, said the WHO.

Such statistics mean that specialised healthcare treatment in rural India is difficult, which has driven rising numbers of people to costlier private healthcare.

(With inputs from IANS)

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AI Can Better Help Doctors to Identify Cancer Cells in Human Body

The process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone

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Cancer
The AI algorithm helps pathologists obtain the most accurate Cancer cell analysis - in a much faster way. Pixabay

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

Cancer
Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer Cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)

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