Multi-model deep learning approach for segmentation of teeth and periapical lesions on pantomographs.

INTRODUCTION: The fields of medicine and dentistry are beginning to integrate artificial intelligence (AI) in diagnostics. This may reduce subjectivity and improve the accuracy of diagnoses and treatment planning. Current evidence on pathosis detection on pantomographs (PGs) indicates the presence or absence of disease in the entire radiographic image, with little evidence of the relation of periapical pathosis to the causative tooth. OBJECTIVE: To develop a deep learning (DL) AI model for the segmentation of periapical pathosis and its relation to teeth on PGs. METHOD: 250 PGs were manually annotated by subject experts to lay down the ground truth for training AI algorithms on the segmentation of periapical pathosis. Two approaches were used for lesion detection: Multi-models 1 and 2, using U-net and Mask RCNN algorithms, respectively. The resulting segmented lesions generated on the testing data set were superimposed with results of teeth segmentation and numbering algorithms trained separately to relate lesions to causative teeth. Hence, both multi-model approaches related periapical pathosis to the causative teeth on PGs. RESULTS: The performance metrics of lesion segmentation carried out by U-net are as follows: Accuracy = 98.1%, precision = 84.5%, re-call = 80.3%, F-1 score = 82.2%, dice index = 75.2%, and Intersection over Union = 67.6%. Mask RCNN carried out lesion segmentation with an accuracy of 46.7%, precision of 80.6%, recall of 55%, and F-1 score of 63.1%. CONCLUSION: In this study, the multi-model approach successfully related periapical pathosis to the causative tooth on PGs. However, U-net outperformed Mask RCNN in the tasks performed, suggesting that U-net will remain the standard for medical image segmentation tasks. Further training of the models on other findings and an increased number of images will lead to the automation of the detection of common radiographic findings in the dental diagnostic workflow.

Development and feasibility testing of an artificially intelligent chatbot to answer immunization-related queries of caregivers in Pakistan: A mixed-methods study.

BACKGROUND: Gaps in information access impede immunization uptake, especially in low-resource settings where cutting-edge and innovative digital interventions are limited given the digital inequity. Our objective was to develop an Artificially Intelligent (AI) chatbot to respond to caregiver's immunization-related queries in Pakistan and investigate its feasibility and acceptability in a low-resource, low-literacy setting. METHODS: We developed Bablibot (Babybot), a local language immunization chatbot, using Natural Language Processing (NLP) and Machine Learning (ML) technologies with Human in the Loop feature. We evaluated the bot through a sequential mixed-methods study. We enrolled caregivers visiting the 12 selected immunization centers for routine childhood vaccines. Additional caregivers were reached through targeted text message communication. We assessed Bablibot's feasibility and acceptability by tracking user engagement and technological metrics, and through thematic analysis of in-depth interviews with 20 caregivers. FINDINGS: Between March 9, 2020, and April 15, 2021, 2,202 caregivers were enrolled in the study, of which, 677 (30.7%) interacted with Bablibot (users). Bablibot responded to 1,877 messages through 874 conversations. Conversation topics included vaccination due dates (32.4%; 283/874), side-effect management (15.7%;137/874), or delaying vaccination due to child's illness or COVID-lockdown (16.8%;147/874). Over 90% (277/307) of responses to text-based exit surveys indicated satisfaction with Bablibot. Qualitative analysis showed caregivers appreciated Bablibot's usefulness and provided feedback for further improvement of the system. CONCLUSION: Our results demonstrate the feasibility and acceptability of local-language NLP chatbots in providing real-time immunization information in low-resource settings. Text-based chatbots canminimize the workload on helpline operators, in addition to instantaneously resolving caregiver queries that otherwise lead to delay or default.

Concurrent adult pulmonary tuberculosis prevalence survey using digital radiography and Xpert MTB/RIF Ultra and child interferon-gamma release assay Mycobacterium tuberculosis infection survey in Karachi, Pakistan: a study protocol.

Background: Assessment of the effectiveness of tuberculosis control strategies requires the periodic measurement of M. tuberculosis transmission in populations, which is notoriously difficult. One well-established method is to measure the prevalence of infectious pulmonary tuberculosis in the population which is then repeated at a second time point after a period of 'intervention', such as scale up of the Search-Treat-Prevent strategy of the Zero TB Cities initiative, allowing for a 'before and after' comparison.  Protocol: The concurrent adult pulmonary tuberculosis prevalence survey (using digital radiography and Xpert MTB/RIF Ultra) and child M. tuberculosis infection survey (using QuantiFERON-TB® Gold Plus) will primarily provide a baseline measure of the burden of adult infectious tuberculosis in Karachi and assess whether a large-scale interferon gamma release assay survey in children aged 2 to 4 years is feasible. The target population for the prevalence survey is comprised of a stratified random sample of all adults aged 15 years and above and all children aged 2 to 4 years resident in four districts in Karachi. The survey procedures and analyses to estimate pulmonary tuberculosis prevalence are based on the World Health Organization methodology for tuberculosis prevalence surveys. Ethics and dissemination: The study protocol has been approved by the Interactive Research Development / The Indus Hospital Research Centre Research Ethics Committee in Karachi, Pakistan and the London School of Hygiene & Tropical Medicine Research Ethics Committee. Due to non-representative sampling in this setting, where a large proportion of the population are illiterate and are reluctant to provide fingerprints due to concerns about personal security, verbal informed consent will be obtained from each eligible participant or guardian. Results will be submitted to international peer-reviewed journals, presented at international conferences and shared with participating communities and with the Provincial and National TB programme.

Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models.

Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patient is likely to leave the treatment incomplete, leading to a more lethal, drug resistant form of disease. The World Health Organization (WHO) propagates Directly Observed Therapy Short-course (DOTS) as an effective way to stop the spread of TB in communities with a high burden. But DOTS also adds a significant burden on the financial feasibility of the program. We aim to facilitate TB programs by predicting the outcome of the treatment of a particular patient at the start of treatment so that their health workers can be utilized in a targeted and cost-effective way. The problem was modeled as a classification problem, and the outcome of treatment was predicted using state-of-art implementations of 3 machine learning algorithms. 4213 patients were evaluated, out of which 64.37% completed their treatment. Results were evaluated using 4 performance measures; accuracy, precision, sensitivity, and specificity. The models offer an improvement of more than 12% accuracy over the baseline prediction. Empirical results also revealed some insights to improve TB programs. Overall, our proposed methodology will may help teams running TB programs manage their human resources more effectively, thus saving more lives.

Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study.

BACKGROUND: Despite the availability of free routine immunizations in low- and middle-income countries, many children are not completely vaccinated, vaccinated late for age, or drop out from the course of the immunization schedule. Without the technology to model and visualize risk of large datasets, vaccinators and policy makers are unable to identify target groups and individuals at high risk of dropping out; thus default rates remain high, preventing universal immunization coverage. Predictive analytics algorithm leverages artificial intelligence and uses statistical modeling, machine learning, and multidimensional data mining to accurately identify children who are most likely to delay or miss their follow-up immunization visits. OBJECTIVE: This study aimed to conduct feasibility testing and validation of a predictive analytics algorithm to identify the children who are likely to default on subsequent immunization visits for any vaccine included in the routine immunization schedule. METHODS: The algorithm was developed using 47,554 longitudinal immunization records, which were classified into the training and validation cohorts. Four machine learning models (random forest; recursive partitioning; support vector machines, SVMs; and C-forest) were used to generate the algorithm that predicts the likelihood of each child defaulting from the follow-up immunization visit. The following variables were used in the models as predictors of defaulting: gender of the child, language spoken at the child's house, place of residence of the child (town or city), enrollment vaccine, timeliness of vaccination, enrolling staff (vaccinator or others), date of birth (accurate or estimated), and age group of the child. The models were encapsulated in the predictive engine, which identified the most appropriate method to use in a given case. Each of the models was assessed in terms of accuracy, precision (positive predictive value), sensitivity, specificity and negative predictive value, and area under the curve (AUC). RESULTS: Out of 11,889 cases in the validation dataset, the random forest model correctly predicted 8994 cases, yielding 94.9% sensitivity and 54.9% specificity. The C-forest model, SVMs, and recursive partitioning models improved prediction by achieving 352, 376, and 389 correctly predicted cases, respectively, above the predictions made by the random forest model. All models had a C-statistic of 0.750 or above, whereas the highest statistic (AUC 0.791, 95% CI 0.784-0.798) was observed in the recursive partitioning algorithm. CONCLUSIONS: This feasibility study demonstrates that predictive analytics can accurately identify children who are at a higher risk for defaulting on follow-up immunization visits. Correct identification of potential defaulters opens a window for evidence-based targeted interventions in resource limited settings to achieve optimal immunization coverage and timeliness.

An evaluation of systematic tuberculosis screening at private facilities in Karachi, Pakistan.

BACKGROUND: In Pakistan, like many Asian countries, a large proportion of healthcare is provided through the private sector. We evaluated a systematic screening strategy to identify people with tuberculosis in private facilities in Karachi and assessed the approaches' ability to diagnose patients earlier in their disease progression. METHODS AND FINDINGS: Lay workers at 89 private clinics and a large hospital outpatient department screened all attendees for tuberculosis using a mobile phone-based questionnaire during one year. The number needed to screen to detect a case of tuberculosis was calculated. To evaluate early diagnosis, we tested for differences in cough duration and smear grading by screening facility. 529,447 people were screened, 1,010 smear-positive tuberculosis cases were detected and 942 (93.3%) started treatment, representing 58.7% of all smear-positive cases notified in the intervention area. The number needed to screen to detect a smear-positive case was 124 (prevalence 806/100,000) at the hospital and 763 (prevalence 131/100,000) at the clinics; however, ten times the number of individuals were screened in clinics. People with smear-positive TB detected at the hospital were less likely to report cough lasting 2-3 weeks (RR 0.66 95%CI [0.49-0.90]) and more likely to report cough duration >3 weeks (RR 1.10 95%CI [1.03-1.18]). Smear-positive cases at the clinics were less likely to have a +3 grade (RR 0.76 95%CI [0.63-0.92]) and more likely to have +1 smear grade (RR 1.24 95%CI [1.02-1.51]). CONCLUSIONS: Tuberculosis screening at private facilities is acceptable and can yield large numbers of previously undiagnosed cases. Screening at general practitioner clinics may find cases earlier than at hospitals although more people must be screened to identify a case of tuberculosis. Limitations include lack of culture testing, therefore underestimating true TB prevalence. Using more sensitive and specific screening and diagnostic tests such as chest x-ray and Xpert MTB/RIF may improve results.