Epidemiology and outcomes of burn injuries at a tertiary burn care center in Bangladesh

Bailey, M.E.; Sagiraju, Hari Krishna Raju; Mashreky, Saidur Rahman; Alamgir, Hasanat

doi:10.1016/j.burns.2018.12.011

Cited by 48 publications

(38 citation statements)

References 23 publications

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“…In Tansen Municipality of Palpa district, there are 8,433 households with a popula on of 31,161 of which 15,332 are males and 14 15,829 are females. Literatures suggest adult females and under five children are more vulnerable popula on to 4,[15][16][17] sustain burn injuries.…”

Section: Discussionmentioning

confidence: 99%

Spectrum of Unnatural Deaths in Palpa, Nepal: Autopsy Based Study

Atreya¹,

Nepal²,

Gyawali³

2019

Birat J. Health Sci.

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Introduction: Analysis of unnatural death of a community reflects the cause of death and socio-economic strata of that society. Medico-legal autopsies are conducted under the ordinance of legal authority in all cases of unnatural deaths. Objectives: The present study is aimed to determine the spectrum of unnatural deaths in Tansen Municipality of Papla district of Province 5, Nepal. Methodology: This autopsy based study was conducted during the Nepalese fiscal calendar 2074/75 which includes all the cases autopsied in the mortuary of District Hospital Tansen, Palpa. Results: Of total 184 medico-legal autopsies road traffic accidents (RTA) was the leading cause of death (n=53, 28.8%) followed by hanging (n=43, 23.41%) and burn (n=33, 17.9%). Of the 33 cases of burn 25 were females (13.6%) and 8 males (4.3%). There were 13 cases (7.1%) of obscure autopsy. Conclusion: The present study reflected the spectrum of unnatural deaths in Palpa district of province 5, Nepal which revealed RTA as a leading cause of death, followed by hanging. When gender was taken into account it was revealed that more females succumbed to death due to complications of the burn injuries sustained. Unnatural death due to fatal poisoning was least common finding of the present study although poisoning is one of the leading causes of deliberate self-harm globally. Proposal/program of collaboration of District Hospital with other tertiary care centres of Palpa district is the need of the hour which would provide specific, timely and expert approach to range of medicolegal cases.

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Section: Discussionmentioning

confidence: 99%

Spectrum of Unnatural Deaths in Palpa, Nepal: Autopsy Based Study

Atreya¹,

Nepal²,

Gyawali³

2019

Birat J. Health Sci.

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“…Another report by Reference [2] shows that burn is the most common injury affecting children below the age of five in England, causing about 7000 admissions in hospitals each year. Many research reports [3][4][5] have lamented a worrisome situation in poor countries where high cases (up to 90%) of burns injuries are recorded with high percentage rate of mortality and was attributed to lack of social awareness programs related to prevention and safety measures, while most of the cost-effective and safety measures are practically implemented in high income countries [1]. In general, studies [6,7] have shown that about 265,000 deaths are reported every year, as such burns have become eminent health concern globally.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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“…Another report by [2] shows that burn is the most common injury affecting children below the age of five in England causing about 7000 admissions in hospitals each year. Many research reports [3][4][5] have lamented a worrisome situation in poor countries where high cases (up to 90%) of burns injuries are recorded with high percentage rate of mortality. The negative situation in those countries was attributed to lack of social awareness programs related to prevention and safety measures, while most of the cost-effective and safety measures are practically implemented in high income countries [5].…”

Section: Introductionmentioning

confidence: 99%

“…Many research reports [3][4][5] have lamented a worrisome situation in poor countries where high cases (up to 90%) of burns injuries are recorded with high percentage rate of mortality. The negative situation in those countries was attributed to lack of social awareness programs related to prevention and safety measures, while most of the cost-effective and safety measures are practically implemented in high income countries [5]. In general, studies [6,7] have shown that about 265,000 deaths are reported every year, as such burns have become eminent health concern globally.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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Epidemiology and outcomes of burn injuries at a tertiary burn care center in Bangladesh

Cited by 48 publications

References 23 publications

Spectrum of Unnatural Deaths in Palpa, Nepal: Autopsy Based Study

Spectrum of Unnatural Deaths in Palpa, Nepal: Autopsy Based Study

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

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