Evaluating PET-CT in the detection and management of recurrent cervical cancer: Systematic reviews of diagnostic accuracy and subjective elicitation

Background: Positron emission tomography-computed tomography (PET-CT) is recommended to triage women for exenterative surgery and surveillance after treatment for advanced cervical cancer. Objective: To evaluate diagnostic accuracy of additional whole body PET-CT compared with CT/magnetic resonance imaging (MRI) alone in women with suspected recurrent/persistent cervical cancer and in asymptomatic women as surveillance. Design: Systematic reviews. Subjective elicitation to supplement diagnostic information. Search strategy/Selection criteria/Data collection and analysis: Searches of electronic databases were performed to June 2013. Studies in women with suspected recurrent/persistent cervical cancer and in asymptomatic women undergoing follow up with sufficient numeric data were included. We calculated sensitivity, specificity and corresponding 95% confidence intervals. Meta-analyses employed a bivariate model that included a random-effects term for between-study variations (CT studies) and univariate random effects meta-analyses (PET-CT studies) for sensitivity and specificity separately. Subjective elicitation: Prevalence of recurrence and the accuracy of imaging elicited using the allocation of points technique. Coherence of elicited subjective probabilities with estimates in the literature examined. Results: We identified 15 relevant studies; none directly compared additional PET-CT with MRI or CT separately. Most CT and MRI studies used older protocols and the majority did not distinguish between asymptomatic and symptomatic women. Meta-analysis of nine PET-CT studies in mostly symptomatic women showed sensitivity of 94.8 (95% CI 91.2-96.9), and specificity of 86.9% (95% CI 82.2-90.5). The summary estimate of the sensitivity of CT for detection of recurrence was 89.64% (95% CI 81.59-94.41) and specificity was 76% (95% CI 43.68-92.82). Meta-analysis for MRI test accuracy studies was not possible because of clinical heterogeneity. The sensitivity and specificity of MRI in pelvic recurrence varied between 82 and 100% and between 78 and 100%, respectively. Formal statistical comparisons of the accuracy of index tests were not possible. Subjective elicitation provided estimates comparable to the literature. Subjective estimates of the increase in accuracy from the addition of PET-CT were less than elicited increases required to justify the use in PET-CT for surveillance. Conclusion: Evidence to support additional PET-CT is scarce, of average quality and does not distinguish between application for surveillance and diagnosis. Guidelines recommending PET-CT in recurrent cervical cancer need to be reconsidered in the light of the existing evidence base

Adversarial Machine Learning For Advanced Medical Imaging Systems

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems. We start by describing our advanced medical imaging systems to tackle the major challenges of on-device deployment: automation, uncertainty, and resource constraint. It is followed by novel unsupervised and semi-supervised robust training schemes to enhance the adversarial robustness of these medical imaging systems. These methods are designed to tackle the unique challenges of defending against adversarial attacks on medical imaging systems and are sufficiently flexible to generalize to various medical imaging modalities and problems. We continue on developing novel training scheme to enhance adversarial robustness of the general DNN based natural image classification models. Based on a unique insight into the predictive behavior of DNNs that they tend to misclassify adversarial samples into the most probable false classes, we propose a new loss function as a drop-in replacement for the cross-entropy loss to improve DNN\u27s adversarial robustness. Specifically, it enlarges the probability gaps between true class and false classes and prevents them from being melted by small perturbations. Finally, we conclude the dissertation by summarizing original contributions and discussing our future work that leverages DNN interpretability constraint on adversarial training to tackle the central machine learning problem of generalization gap

Um sistema de teleoftalmologia para triagem de urgências em áreas remotas do Brasil

Purposes: To validate a teleophthalmology mobile system aimed at improving and providing eye urgency screenings in remote and poor area settings in Brazil. The system enables one or more ophthalmologists to remotely examine a patient's condition and submit a decision describing the gravity of the case. If necessary, the patient can be forwarded to a hospital for further consultation. Methods: A cellphone (Nexus One model, with a 5 megapixel camera) was used to collect data and pictures from 100 randomly selected patients at the Ophthalmology Emergency Room located at the General Hospital of the Federal University of São Paulo (UNIFESP). Data was then sent remotely to an online recording system to be reviewed by an ophthalmologist who provided feedback regarding the state of ocular urgency. Results were then compared to the gold standard diagnosis provided at the hospital. Results: The diagnosis of urgency was given by two ophthalmologists: one in the hospital (gold standard) and one remotely. When we compared both diagnoses we obtained results of 81.94% specificity, 92.85% sensitivity, and 85% accuracy, with a negative predictive value of 96.72%. This work also included a processing time analysis, resulting in an average time of 8.6 min per patient for remote consultations. Conclusions: This study is the first that has used only a cellphone for diagnosing the urgency of ocular cases. Based on our results, the system can provide a reliable distinction between urgent and non-urgent situations and can offer a viable alternative for the servicing of underprivileged areas. In screening techniques, the most important outcome is to identify urgent cases with a high level of sensitivity and predictive negative value. Thus, our results demonstrate that this tool is robust and we suggest that a major study aimed to verify its efficiency in resource-poor areas should be initiated.Objetivos: Validar um sistema de teleoftalmologia móvel que tem como objetivo fornecer triagem de urgências oftalmológicas em áreas remotas e desfavorecidas do Brasil. O sistema permite que um ou mais oftalmologistas possam examinar remotamente a condição do paciente e apresentar uma decisão que descreve a gravidade do caso. Se necessário, o paciente será encaminhado ao hospital para consulta. Métodos: Através de um celular e sua câmera (modelo Nexus One com câmera de 5 megapixel), foram coletados dados de 100 pacientes aleatoriamente selecionados no pronto socorro oftalmológico da Universidade Federal de São Paulo (UNIFESP) e enviados remotamente para um sistema online, por meio do qual um oftalmologista analisou-os e enviou um diagnóstico sobre a gravidade dos casos. Os resultados enviados foram comparados com o padrão ouro que foi fornecido pelo médico no hospital. Resultados: O diagnóstico foi fornecido por dois oftalmologistas: um no hospital (padrão outro) e outro remotamente. Comparando os resultados dos diagnósticos, foi obtido 81,94% de especificidade, 92,85% de sensibilidade, acurácia de 85% e um valor preditivo negativo de 96,72%. Também foi testado o desempenho do sistema, resultando num tempo de processamento do atendimento remoto em média de 8,6 minutos por paciente. Conclusões: Esta solução de baixo custo é a primeira da literatura que utiliza apenas o telefone celular para detectar urgências oftalmológicas. Com base nos resultados, o sistema consegue oferecer um atendimento confiável, diferenciando os casos de urgências e não urgências através da plataforma de telemedicina. Como ferramenta de triagem, o mais importante é identificar os casos de urgência (ter alta sensibilidade). Assim, os resultados obtidos demonstram que a ferramenta é robusta e traz uma possibilidade real de conduzir um estudo maior para verificar sua eficiência em áreas mais distantes e desfavorecidas.Universidade Federal do Rio Grande do NorteMassachusetts Institute of TechnologyUniversidade Federal de São Paulo (UNIFESP) Department of OphthalmologyHarvard Medical School (Schepens Eye Research Institute) Department of OphthalmologyUniversidade Federal de São Paulo (UNIFESP) Retina DepartmentUNIFESP, Department of OphthalmologyUNIFESP, Retina DepartmentSciEL

Respiratory rate measurement in children using a thermal camera

Abstract— Respiratory rate is a vital physiological measurement used in the immediate assessment of unwell children. Con-venient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although de-vices which measure respiratory rate exist, none has entered everyday clinical practice. An accurate device which has no physical contact with the child is important to ensure readings are not affected by distress. A thermal imaging camera to moni-tor respiratory rate in children was evaluated. Facial thermal images of 20 children (age: median=6.5 years, range 6 months-17 years) were included in the study. Record-ings were performed while the children slept comfortably on a bed for a duration of two minutes. Values obtained using the thermal imaging camera were compared with those obtained from standard methods: nasal thermistor, respiratory impedance plethysmography and transcutaneous CO2. Median respiratory rate measurements per minute were 21.0 (range 15.5-34.0) using thermal imaging and 19.0 (range 15.3-34.0) using standard methods. A close correlation (r 2 = 0.994) was observed between the thermal imaging and the standard methods. The thermal imaging camera is an accurate, objective non-invasive device which can be used to measure respiratory rate in children

The use of computerised clinical decision support systems in emergency care : a substantive review of the literature

Objectives: This paper provides a substantive review of international literature evaluating the impact of computerised clinical decision support systems (CCDSS) on the care of emergency department (ED) patients. Material and Methods: A literature search was conducted using Medline, CINAHL, EMBASE electronic resources and grey literature. Studies were selected if they compared the use of a CCDSS with usual care in a face-to-face clinical interaction in an ED. Results: Of the 23 studies included approximately half demonstrated a statistically significant positive impact on aspects of clinical care with the use of CCDSSs. The remaining studies showed small improvements, mainly around documentation. However, the methodological quality of the studies was poor with few or no controls to mitigate against confounding variables. The risk of bias was high in all but six studies. Discussion: The ED environment is complex and does not lend itself to robust quantitative designs such as Randomised Controlled Trials. The quality of the research in approximately 75% of the studies was poor and therefore conclusions cannot be drawn from these results. However the studies with a more robust design show evidence of the positive impact of CCDSSs on ED patient care. Conclusion This is the first review to consider the role of CCDSSs in emergency care and expose the research in this area. The role of CCDSSs in Emergency Care may provide some solutions to the current challenges in EDs but further high quality research is needed to better understand what technological solutions can offer clinicians and patients. OBJECTIVES This paper provides a description of a substantive review of published international literature evaluating the impact of computerised clinical decision support systems (CCDSS) on the care of emergency department (ED) patients. The principal aims of this review are: to identify the body of CCDSS research undertaken in EDs, the research methods used, their quality and the impact of CCDSSs on clinical care in EDs. The discussion synthesises what is known and not known about the effectiveness of CCDSSs in Emergency Care and the quality of the current evidence base

MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications