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Smartphone-based application improves the detection of retinoblastoma.

ABSTRACT: PURPOSE:To improve and validate the smartphone-based leukocoria detection application so that non-ophthalmologists could make use of the smartphone for early detection of Retinoblastoma (RB) in young children without anesthesia and pharmacological dilatation of the pupil. METHODS:Two apps, MDEyeCare and CRADLE, developed for red reflex based leukocoria detection were used in iPhone 6s. MDEyeCare methodology was modified with respect to ambient lighting, the distance between camera and eye and different gazes for better performance. We analyzed totally 34 eyes of 23 RB patients and four normal children. Each of the RB patients was confirmed with clinical examination and radiological investigations. RESULTS:Modification in the methodology of MDEyeCare app could detect the leukocoria in early stages of RB (50% of Group B, 83% of Group C). In late stages (Group D and E), 100% of tumors were detected. The CRADLE app failed to provide adequate leukocoria detection except four late stage RB eyes. Among the 14 normal eyes (6 from unilateral RB and eight from normal children), pseudo-leukocoria was observed in three eyes only at lateral gaze even with MDEyeCare app. CONCLUSION:Improved methodology in smartphone-based app enhanced the detection of RB and this may translate into better outcome after treatment with respect to vision salvage.

SUBMITTER: Khedekar A

PROVIDER: S-EPMC6707147 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Smartphone-based application improves the detection of retinoblastoma.

Khedekar Amit A Devarajan Bharanidharan B Ramasamy Kim K Muthukkaruppan Veerappan V Kim Usha U

Eye (London, England) 20190111 6

<h4>Purpose</h4>To improve and validate the smartphone-based leukocoria detection application so that non-ophthalmologists could make use of the smartphone for early detection of Retinoblastoma (RB) in young children without anesthesia and pharmacological dilatation of the pupil.<h4>Methods</h4>Two apps, MDEyeCare and CRADLE, developed for red reflex based leukocoria detection were used in iPhone 6s. MDEyeCare methodology was modified with respect to ambient lighting, the distance between camera ...[more]

PMID: 30635644

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Project description:BackgroundFueled by rapid technological advances over the past decade, there is growing interest in the use of smartphones to aid in smoking cessation. Hundreds of applications have been developed for this purpose, but little is known about how these applications are accessed and used by smokers or what features smokers believe would be most useful.PurposeThe present study sought to understand the prevalence of smartphone ownership and patterns of use among smokers as well as the perceived utility of various smartphone application features for smoking cessation that are currently in development or already available.MethodsDaily cigarette smokers (n = 224) reported on smartphone ownership, their patterns of smartphone usage, and perceived utility of features. Features were ranked according to perceived utility and differences in both perceived utility and general smartphone use patterns were examined as a function of demographic and smoking-related variables.ResultsMost smokers (80.4%) own a smartphone, but experience with smoking cessation applications is extremely rare (6.1%). Ownership and patterns of usage differed as a function of demographic and smoking-related variables. Overall, gain-framed features were rated as most useful, while loss-framed and interpersonal features were rated as least useful.ConclusionsMobile health interventions have the potential to reach a large number of smokers but are currently underutilized. Additional effort is needed to ensure parity in treatment access. Gain-framed messages may be especially useful for engaging smokers, even if other features ultimately drive treatment effects.ImplicationsThis study describes patterns of smartphone usage among smokers and identifies the smartphone application features smokers believe would be most useful during a quit attempt. Findings indicate which subgroups of smokers are most likely to be reached with mobile health interventions and suggests that inclusion of specific features may be helpful for engaging smokers in the smoking cessation process.

Project description:BackgroundMicroscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.MethodsA total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.ResultsMalaria Screener reached 74.1% (95% CI 63.5-83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0-81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8-96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0-88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6-86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.ConclusionMalaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.

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Smartphone-based application improves the detection of retinoblastoma.

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Smartphone-based application improves the detection of retinoblastoma.

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