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1. Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map

Author

Song, Bofan, Zhang, Chicheng, Sunny, Sumsum, Kc, Dharma Raj, Li, Shaobai, Gurushanth, Keerthi, Mendonca, Pramila, Mukhia, Nirza, Patrick, Sanjana, Gurudath, Shubha, Raghavan, Subhashini, Tsusennaro, Imchen, Leivon, Shirley T, Kolur, Trupti, Shetty, Vivek, Bushan, Vidya, Ramesh, Rohan, Pillai, Vijay, Wilder-Smith, Petra, Suresh, Amritha, Kuriakose, Moni Abraham, Birur, Praveen, and Liang, Rongguang

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Behavioral and Social Science, Cancer, Basic Behavioral and Social Science, visual explanation, attention mechanism, human-in-the-loop deep learning, attention map, expert knowledge embedding, attention branch network, Oncology and carcinogenesis
Abstract

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.

Published
2023

2. Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images

Author

Song, Bofan, Li, Shaobai, Sunny, Sumsum, Gurushanth, Keerthi, Mendonca, Pramila, Mukhia, Nirza, Patrick, Sanjana, Peterson, Tyler, Gurudath, Shubha, Raghavan, Subhashini, Tsusennaro, Imchen, Leivon, Shirley T, Kolur, Trupti, Shetty, Vivek, Bushan, Vidya, Ramesh, Rohan, Pillai, Vijay, Wilder-Smith, Petra, Suresh, Amritha, Kuriakose, Moni Abraham, Birur, Praveen, and Liang, Rongguang

Subjects
Biomedical and Clinical Sciences, Engineering, Biomedical Engineering, Physical Sciences, Ophthalmology and Optometry, Atomic, Molecular and Optical Physics, Cancer, Dental/Oral and Craniofacial Disease, Humans, Uncertainty, Semantics, Bayes Theorem, Reproducibility of Results, Neural Networks, Computer, Image Processing, Computer-Assisted, Mouth Neoplasms, uncertainty measures of deep learning, oral cancer, semantic segmentation, Monte Carlo dropout, Bayesian deep learning, Optical Physics, Opthalmology and Optometry, Optics, Ophthalmology and optometry, Biomedical engineering, Atomic, molecular and optical physics
Abstract

SignificanceOral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.AimWe aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.ApproachThis work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.ResultsThe proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.ConclusionsOur study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved.

Published
2022

3. Bayesian deep learning for reliable oral cancer image classification.

Author

Song, Bofan, Sunny, Sumsum, Li, Shaobai, Gurushanth, Keerthi, Mendonca, Pramila, Mukhia, Nirza, Patrick, Sanjana, Gurudath, Shubha, Raghavan, Subhashini, Tsusennaro, Imchen, Leivon, Shirley T, Kolur, Trupti, Shetty, Vivek, Bushan, Vidya R, Ramesh, Rohan, Peterson, Tyler, Pillai, Vijay, Wilder-Smith, Petra, Sigamani, Alben, Suresh, Amritha, Kuriakose, Moni Abraham, Birur, Praveen, and Liang, Rongguang

Subjects
Dental/Oral and Craniofacial Disease, Rare Diseases, Cancer, Basic Behavioral and Social Science, Behavioral and Social Science, Optical Physics, Materials Engineering
Abstract

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.

Published
2021

4. Classification of imbalanced oral cancer image data from high-risk population.

Author

Song, Bofan, Li, Shaobai, Sunny, Sumsum, Gurushanth, Keerthi, Mendonca, Pramila, Mukhia, Nirza, Patrick, Sanjana, Gurudath, Shubha, Raghavan, Subhashini, Tsusennaro, Imchen, Leivon, Shirley T, Kolur, Trupti, Shetty, Vivek, Bushan, Vidya, Ramesh, Rohan, Peterson, Tyler, Pillai, Vijay, Wilder-Smith, Petra, Sigamani, Alben, Suresh, Amritha, Kuriakose, Moni Abraham, Birur, Praveen, and Liang, Rongguang

Subjects
deep learning, ensemble learning, imbalanced multi-class datasets, mobile screening device, oral cancer, Algorithms, Early Detection of Cancer, Humans, Machine Learning, Mouth Neoplasms, Neural Networks, Computer, Clinical Research, Bioengineering, Rare Diseases, Dental/Oral and Craniofacial Disease, Cancer, Health Services, Prevention, Optics, Optical Physics, Biomedical Engineering, Opthalmology and Optometry
Abstract

SignificanceEarly detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification.AimTo reduce the class bias caused by data imbalance.ApproachWe collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings.ResultsBy applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of "premalignancy" class is also increased, which is ideal for screening applications.ConclusionsExperimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Published
2021

5. Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders

Author

Praveen Birur N., Bofan Song, Sumsum P. Sunny, Keerthi G., Pramila Mendonca, Nirza Mukhia, Shaobai Li, Sanjana Patrick, Shubha G., Subhashini A.R., Tsusennaro Imchen, Shirley T. Leivon, Trupti Kolur, Vivek Shetty, Vidya Bhushan R., Daksha Vaibhavi, Surya Rajeev, Sneha Pednekar, Ankita Dutta Banik, Rohan Michael Ramesh, Vijay Pillai, Kathryn O.S., Petra Wilder Smith, Alben Sigamani, Amritha Suresh, Rongguang Liang, and Moni A. Kuriakose

Subjects
Medicine, Science
Abstract

Abstract Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings.

Published
2022
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6. Inter-observer agreement among specialists in the diagnosis of Oral Potentially Malignant Disorders and Oral Cancer using Store-and-Forward technology

Author

Keerthi Gurushanth, Nirza Mukhia, Sumsum P Sunny, Bofan Song, Shubhasini A Raghavan, Shubha Gurudath, Pramila Mendonca, Shaobai Li, Sanjana Patrick, Tsusennaro Imchen, Shirley T. Leivon, Tulika Shruti, Trupti Kolur, Vivek Shetty, Vidya Bhushan R, Rohan Michael Ramesh, Vijay Pillai, Kathryn O. S, Petra Wilder Smith, Amritha Suresh, Rongguang Liang, Praveen Birur N, and Moni A. Kuriakose

Abstract

Oral Cancer is one of the most common causes of morbidity and mortality. Screening and mobile Health (mHealth) based approach facilitates remote early detection of Oral cancer in a resource-constrained settings. The emerging eHealth technology has aided specialist reach to rural areas enabling remote monitoring and triaging to downstage Oral cancer. Though the diagnostic accuracy of the remote specialist has been evaluated, there are no studies evaluating the consistency among the remote specialists, to the best of our knowledge. The purpose of the study was to evaluate the interobserver agreement between the specialists through telemedicine systems in real-world settings using store and forward technology. Two remote specialists independently diagnosed the clinical images from image repositories, and the diagnostic accuracy was compared with onsite specialist and histopathological diagnosis when available. Moderate agreement (k = 0.682) between two remote specialists and (k = 0.629) between the onsite specialist and two remote specialists in diagnosing oral lesions. The sensitivity and specificity of remote specialist 1 were 92.7% and 83.3%, whereas remote specialist 2 was 95.8% and 60%, respectively, compared to histopathology. The store and forward technology and telecare can be effective tools in triaging and surveillance of patients.

Published
2023

7. PREVALENCE OF SHARP INJURY AMONG STAFF NURSES IN A TERTIARY HOSPITAL: A CROSS SECTIONAL STUDY

Author

Tsusennaro Imchen, Kumari, Reema, and Shradha Mishra

Subjects
Sharp Injury Universal Precaution Post-Exposure Prophylaxis Occupational Hazards
Abstract

Background:Healthcare settings pose a major occupational hazard to their employees. Needlestick and sharps injuries (NSSIs) are one of the major risk factors for blood-borne infection among healthcare workers. Materials and Method:A hospital-based cross-sectional study was carried out in a tertiary healthcare hospital in Lucknow district with the objectives of assessing the prevalence of sharp injury among staff nurses and the safety precautions taken after the occurrence of sharp injury. The study participants included the staff nurses at various departments of the hospital who were randomly selected for the study. Thus a total of 85 staff nurses were selected and interviewed after taking verbal consent. Results and Discussion:The majority (54.1%) of the staff nurses were in the age group of 26-35 years. Out of the total, 32.9% had an incident of sharp injury. Needle (67.9%) accounted for the maximum injury. Of the total that got injured, injury occurred during the use of sharp item (82.1%) and of the total that got injured, only 28.6% took post-exposure prophylaxis as injection TT. Conclusion:In the present study almost half of the participants had an occurrence of sharp injury over the past one year. And poor post exposure prophylaxis practices were observed. &nbsp

Published
2023
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8. Interpretable deep learning approach for oral cancer classification using guided attention inference network

Author

Kevin Chew Figueroa, Bofan Song, Sumsum Sunny, Shaobai Li, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Shubha Gurudath, Subhashini Raghavan, Tsusennaro Imchen, Shirley T. Leivon, Trupti Kolur, Vivek Shetty, Vidya Bushan, Rohan Ramesh, Vijay Pillai, Petra Wilder-Smith, Alben Sigamani, Amritha Suresh, Moni Abraham Kuriakose, Praveen Birur, and Rongguang Liang

Subjects
Paper, Neural Networks, Biomedical Engineering, Reproducibility of Results, Optics, guided attention inference network, Optical Physics, oral cancer, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Biomaterials, Computer, Deep Learning, Opthalmology and Optometry, Humans, interpretable deep learning, Attention, Mouth Neoplasms, Neural Networks, Computer, General, Cancer
Abstract

Significance: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network’s attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. Aim: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. Approach: We utilized Selvaraju et al.’s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.’s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. Results: The network’s attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. Conclusions: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.

Published
2022

9. Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders

Author

Praveen Birur N., Bofan Song, Sumsum P. Sunny, Keerthi G., Pramila Mendonca, Nirza Mukhia, Shaobai Li, Sanjana Patrick, Shubha G., Subhashini A.R., Tsusennaro Imchen, Shirley T. Leivon, Trupti Kolur, Vivek Shetty, Vidya Bhushan R., Daksha Vaibhavi, Surya Rajeev, Sneha Pednekar, Ankita Dutta Banik, Rohan Michael Ramesh, Vijay Pillai, Kathryn O.S., Petra Wilder Smith, Alben Sigamani, Amritha Suresh, Rongguang Liang, and Moni A. Kuriakose

Subjects
Multidisciplinary, Deep Learning, Point-of-Care Systems, Humans, Mouth Neoplasms, Cell Phone, Early Detection of Cancer, Telemedicine
Abstract

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings.

Published
2021

10. Mobile-based oral cancer classification for point-of-care screening

Author

Shubha Gurudath, Rohan Ramesh, Keerthi Gurushanth, Praveen Birur, Sanjana Patrick, Vijay Pillai, Tsusennaro Imchen, Rongguang Liang, Vivek Shetty, Trupti Kolur, Pramila Mendonca, Moni Abraham Kuriakose, Amritha Suresh, Vidya Bushan, Petra Wilder-Smith, Bofan Song, Alben Sigamani, Natzem Lima, Subhashini Raghavan, Nirza Mukhia, Shirley T Leivon, Shaobai Li, and Sumsum P. Sunny

Subjects
Paper, business.product_category, dual-modality, Computer science, Point-of-Care Systems, Biomedical Engineering, mobile screening device, Image processing, Bioengineering, Optical Physics, Machine learning, computer.software_genre, 01 natural sciences, Sensitivity and Specificity, efficient deep learning, 010309 optics, Biomaterials, Clinical Research, Opthalmology and Optometry, 0103 physical sciences, Internet access, Humans, Android (operating system), Dental/Oral and Craniofacial Disease, General, Early Detection of Cancer, Point of care, Cancer, Contextual image classification, business.industry, Deep learning, Prevention, Optics, Gold standard (test), oral cancer, Health Services, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, 4.1 Discovery and preclinical testing of markers and technologies, The Internet, Mouth Neoplasms, Artificial intelligence, Smartphone, business, computer, 4.2 Evaluation of markers and technologies
Abstract

Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Published
2021

11. mHealth-Based Point-Of-Care Diagnostic Tool for Early Detection of Oral Cancer and Pre-Cancer Lesions in a Low-Resource Setting

Author

Ankita Dutta Banik, Shubha G, Rohan Ramesh, Sneha Pednekar, Praveen Birur N, Trupti Kolur, Amritha Suresh, Petra Wilder Smith, Surya Rajeev, Bofan Song, Nirza Mukhia, Shaobai Li, Vidya Bushan R, Vijay Pillai, Moni Abraham Kuriakose, Alben Sigamani, Tsusennaro Imchen, Shubhashini Ar, Sanjana Patrick, Rongguang Liang, Pramila Mendonca, Kathryn Osann, Daksha Vaibhavi, Sumsum P. Sunny, Vivek Shetty, Keerthi G, and Shirley T Leivon

Subjects
medicine.medical_specialty, Low resource, business.industry, Early detection, Cancer, Institutional ethics, medicine.disease, Clinical trial, Family medicine, Medicine, Medical diagnosis, business, mHealth, Point of care
Abstract

Background: Early detection of oral cancer in low-resource settings necessitates a robust, Point-of-Care screening tool that can empower Frontline-Health-Workers (FHW) for triaging high-risk populations. This study was conducted to validate the accuracy of Artificial-Neural-Network (ANN) enabled m(mobile)-Health device deployed with FHWs. Methods: The dual-imaging device enables white-light as well as auto-fluorescence imaging with both a wide and focused view. The effectiveness of the device was tested in tertiary-care/dental hospitals and low-resource settings of South/North-East India. Demographics and clinical details were documented in the device, and the subjects were screened independently, either by FHWs or along with specialists, in addition to a remote evaluation by oral cancer specialists. Simple and complex ANN were built using the images for classification of oral-potentially-malignant/malignant lesions and integrated into the mobile phone/cloud. The specialist diagnoses were compared with the histopathology, FHW, and ANN-based diagnosis to evaluate the efficacy of the oral cancer detection program. Findings: The program screened 5025 subjects with 95% (n=4728) having tele-diagnosis. Among the 16% (n=752) assessed by onsite specialists, biopsy was advised for 515, out of which 20% (n=102) underwent biopsy. The onsite specialist diagnosis showed high sensitivity (94%) and moderate specificity (72%) when compared to histology diagnosis, while tele-diagnosis showed high accuracy when compared with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, when compared with tele-diagnosis, identified suspicious lesions with less sensitivity (60%). Further, the efficacy of the FHWs in delineating suspicious lesions correlated (r= 0·80) with their prior experience in the project. The ANN (MobileNet) integrated with the phone for a real-time diagnosis could accurately delineate lesions (n=1416; sensitivity: 82%), while the cloud-based ANN (VGG19) had higher accuracy (sensitivity: 87%) when compared to tele-diagnosis. Interpretation: This study suggests that an automated mHealth-enabled dual-image system could prove a useful triaging tool, empowering FHWs towards accurate screening in low-resource settings. Clinical Trial: The study protocol was registered in the Clinical Trial Registry of the Indian Council of Medical Research (CTRI/2019/11/022167). Funding: This study is primarily funded by the National Institutes of Health (NIH), USA grants (UH2EB022623, UH3CA239682). This work is also supported by LAMMP NIH/NIBIB P41EB05890, Arnold and Mabel Beckman Foundation, California Tobacco-Related Diseases Program: T31IR1825. Declaration of Interest: We declare no competing interests. Ethical Approval: Institutional Ethics Committee approvals were obtained from the three nodal centers- The KLE Society’s Institute of Dental Sciences (KLE; ECR/887/Inst/KA/2016), Bengaluru, India, Christian Institute of Health Sciences and Research (CIHSR; EC/NEW/INST/2020/782), Dimapur, Nagaland, India, and Mazumdar Shaw Medical Center (MSMC; NNH/MEC-CL-2016-394), Bengaluru, India prior to the initiation of the clinical trial.

Published
2021

12. Study of biomedical waste management among healthcare personnel at a Tertiary hospital in Lucknow district

Author

Reema Kumari, Tsusennaro Imchen, Jaypal Singh, Kirti Srivastava, and Anshita Singh

Subjects
Nursing staff, business.industry, Cross-sectional study, Retraining, Biomedical waste, Hospital based, medicine.disease, 03 medical and health sciences, 0302 clinical medicine, Nursing, Hazardous waste, Hepatitis b vaccination, Health care, Medicine, 030212 general & internal medicine, Medical emergency, business, 030217 neurology & neurosurgery
Abstract

Background: Healthcare facilities are like a double-edged sword, it caters to the healthcare needs and problems of the people by providing curative, promotive or preventive services but in the process it inevitably produces waste which in itself is hazardous to health if not managed properly. In order to improve biomedical waste management, it is important to understand and evaluate the current practices in biomedical waste management, to identify the gaps and to address them . The study was conducted to assess the practice of biomedical waste management among the healthcare personnel at a tertiary hospital. Methods: A hospital based cross sectional study was conducted among 314 healthcare personnel which comprised of 193 doctors, 85 staff nurses and 36 laboratory technicians. A pre-tested semi-structured questionnaire was used to collect the data . Results: A total of 78% healthcare personnel had received training on BMW management. Most of the doctors (76.2%), staff nurses (70.6%) and laboratory technicians (72.2%) had received hepatitis B vaccination. And as for injection TT, 76.2% doctors, 85.9% staff nurses and 69.4% laboratory technicians had received it. Multivariate logistic regression showed association between waste segregation practices and occupation status and training which was statistically significant . Conclusions: The study revealed satisfactory practices among the healthcare personnel. It also showed association between waste segregation practices and training on BMW management. The importance of training regarding biomedical waste management cannot be overemphasized, training and retraining on healthcare waste management should be given to healthcare personnel .

Published
2017

13. Housing and sanitary conditions in slums of Lucknow, capital of Uttar Pradesh

Author

Mukesh Shukla, Tsusennaro Imchen, Monika Agarwal, Hossain Mehedi Rehman, and Kriti Yadav

Subjects
Health problems, Industrialisation, Sanitation, business.industry, Urbanization, Capital (economics), Continuous type, Medicine, Garbage disposal, business, Uttar pradesh, Socioeconomics
Abstract

Background: Slums have been part of the history of most cities, particularly in the early years of industrialization. Rapid urbanization and the mismatch in the provision and maintenance of proper housing conditions result in environmental decay, which further leads to origin and spread of health problems and diseases. Objective: To study the housing and sanitary conditions of the households residing in urban slums of Lucknow. Materials and Methods: A community-based cross-sectional study was conducted in urban slums of Lucknow city from February 2014 to September 2014. A total of 384 households were assessed for housing and sanitary conditions through house-to-house survey with the help of predesigned, pretested, and semistructured questionnaire. Result: Among 384 households included in the study, 77.1% were situated in congested locality with 69.5% having a back to back and continuous type of house setup. About 47.3% and 25.0% of the houses were having inadequate ventilation and lighting, respectively. A total of 74.5% of the houses had unsafe practices for water storage and handling. The practice of handwashing before cooking food and eating was found in 77.9% and 52.6%, respectively. A total of 51.1% of the households reported open field defecation. Conclusion: This study reveals the need for developing and providing a more effective and comprehensive package of facilities related to housing, water, sanitation, and garbage disposal for urban slums.

Published
2016

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