Computer-aided segmentation and estimation of indices in brain CT scans

The importance of neuro-imaging as one of the biomarkers for diagnosis and prognosis of pathologies and traumatic cases is well established. Doctors routinely perform linear measurements on neuro-images to ascertain severity and extent of the pathology or trauma from significant anatomical changes. However, it is a tedious and time consuming process and manually assessing and reporting on large volume of data is fraught with errors and variation. In this paper we present a novel technique for segmentation of significant anatomical landmarks using artificial neural networks and estimation of various ratios and indices performed on brain CT scans. The proposed method is efficient and robust in detecting and measuring sizes of anatomical structures on non-contrast CT scans and has been evaluated on images from subjects with ages between 5 to 85 years. Results show that our method has average ICC of ≥0.97 and, hence, can be used in processing data for further use in research and clinical environment

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

Assessing Water Consumption of Major Crops in the Command Area of Malwah Distributary, Shaheed Benazirabad, Sindh.

Soil and water are vital natural resources on which agriculture sector growth and village livelihood depend and having the proper knowledge of the Soil, Plant, and water relationship are extremely important to achieve sustainable agricultural productivity. Pakistan has entered the 21st century with the rising challenge to meet food and fiber requirements for its population for domestic consumption and export. Without having appropriate knowledge about the intense water need of plants, most of the agricultural land in Pakistan is still being irrigated by conventional methods, which in turn produces so many problems and reduces the agricultural productivity putting extra stress on the country’s economy, so to avoid these issues, it is extremely necessary to provide the required quantity of water to plant, which will only be possible by consideration and accurate estimation of Evapotranspiration of plant so to enhance awareness and practice of water-saving agriculture in Pakistan to increase the agricultural commodities. In this study, estimation of Actual Evapotranspiration ( ETa ) of Malwah Distributary located in Shaheed Benazirbad, Sindh was selected from Command area of Rohri Canal, ET of four different crops; Cotton, Fallow, Rice and Sugarcane for the period of Rabi 2019-2020 and Kharif 2020 was estimated by using satellite-based evapotranspiration mapping tool namely METRIC REFLUX. The actual ET for each season was obtained using the Reference ET fraction (ETrf) of satellite data and reference ET(ETr) obtained from the literature. The classified crop mask was obtained using maximum likelihood classification on bands 8,4, and 3 of sentinel-2 images of the year 2020. The overall accuracy obtained is 93% with a kappa coefficient 0.921841. The average Actual Evapotranspiration of different crops namely, banana, cotton, rice, and sugarcane were found to be 1527.2 mm, 536.6 mm, 386.80 mm, and 814.02 m

Effects of bomb blast injury on the ears: The Aga Khan University Hospital experience

Abstract Objective: To evaluate the frequency and effects of blast-related otologic injuries. Methods: This retrospective study was conducted at the Aga Khan University Hospital, Karachi, and comprised charts of patients who were victims of bomb explosions between January 2011 and July 2013. Frequency and percentages were reported using cross tabulation with size of bomb, distance of person from blast and the presence of victim in open or closed space. Association of associated variables were also analysed. Results: Of the 100 patients, 81(81%) were men and 19(19%) were women. Besides, 68(68%) patients were aged \u3c30 years. Also, 78(78%) subjects were exposed to \u3c 80kg of explosives and 68(68%) were at a distance of\u3e10m. Furthermore, 61(61%) patients were exposed to explosion in openspace. The prevalence of ear injuries was 21(21%). The odds of experiencing various symptoms of ears was high in those who were exposed to \u3e80 kg of explosives (odds ratio: 3.38; 95% confidence interval, 1.16, 9.91). The odds of hearing loss in those who were within 10m was 8.62 (95% confidence interval: 2.72, 27.28) times than those who were \u3e10 m from the site of explosion. Conclusion: Otologic injuries were frequently associated with large blasts

Can Machine Learning Be Used to Recognize and Diagnose Coughs?

Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.Comment: Accepted in IEEE International Conference on E-Health and Bioengineering - EHB 202

Understanding the influences on successful quality improvement in emergency general surgery: learning from the RCS Chole-QuIC project

Abstract: Background: Acute gallstone disease is the highest volume Emergency General Surgical presentation in the UK. Recent data indicate wide variations in the quality of care provided across the country, with national guidance for care delivery not implemented in most UK hospitals. Against this backdrop, the Royal College of Surgeons of England set up a 13-hospital quality improvement collaborative (Chole-QuIC) to support clinical teams to reduce time to surgery for patients with acute gallstone disease requiring emergency cholecystectomy. Methods: Prospective, mixed-methods process evaluation to answer the following: (1) how was the collaborative delivered by the faculty and received, understood and enacted by the participants; (2) what influenced teams’ ability to improve care for patients requiring emergency cholecystectomy? We collected and analysed a range of data including field notes, ethnographic observations of meetings, and project documentation. Analysis was based on the framework approach, informed by Normalisation Process Theory, and involved the creation of comparative case studies based on hospital performance during the project. Results: Chole-QuIC was delivered as planned and was well received and understood by participants. Four hospitals were identified as highly successful, based upon a substantial increase in the number of patients having surgery in line with national guidance. Conversely, four hospitals were identified as challenged, achieving no significant improvement. The comparative analysis indicate that six inter-related influences appeared most associated with improvement: (1) achieving clarity of purpose amongst site leads and key stakeholders; (2) capacity to lead and effective project support; (3) ideas to action; (4) learning from own and others’ experience; (5) creating additional capacity to do emergency cholecystectomies; and (6) coordinating/managing the patient pathway. Conclusion: Collaborative-based quality improvement is a viable strategy for emergency surgery but success requires the deployment of effective clinical strategies in conjunction with improvement strategies. In particular, achieving clarity of purpose about proposed changes amongst key stakeholders was a vital precursor to improvement, enabling the creation of additional surgical capacity and new pathways to be implemented effectively. Protected time, testing ideas, and the ability to learn quickly from data and experience were associated with greater impact within this cohort

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Decline in subarachnoid haemorrhage volumes associated with the first wave of the COVID-19 pandemic

BACKGROUND: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The study\u27s objective was to examine whether subarachnoid haemorrhage (SAH) hospitalisations and ruptured aneurysm coiling interventions demonstrated similar declines. METHODS: We conducted a cross-sectional, retrospective, observational study across 6 continents, 37 countries and 140 comprehensive stroke centres. Patients with the diagnosis of SAH, aneurysmal SAH, ruptured aneurysm coiling interventions and COVID-19 were identified by prospective aneurysm databases or by International Classification of Diseases, 10th Revision, codes. The 3-month cumulative volume, monthly volumes for SAH hospitalisations and ruptured aneurysm coiling procedures were compared for the period before (1 year and immediately before) and during the pandemic, defined as 1 March-31 May 2020. The prior 1-year control period (1 March-31 May 2019) was obtained to account for seasonal variation. FINDINGS: There was a significant decline in SAH hospitalisations, with 2044 admissions in the 3 months immediately before and 1585 admissions during the pandemic, representing a relative decline of 22.5% (95% CI -24.3% to -20.7%, p\u3c0.0001). Embolisation of ruptured aneurysms declined with 1170-1035 procedures, respectively, representing an 11.5% (95%CI -13.5% to -9.8%, p=0.002) relative drop. Subgroup analysis was noted for aneurysmal SAH hospitalisation decline from 834 to 626 hospitalisations, a 24.9% relative decline (95% CI -28.0% to -22.1%, p\u3c0.0001). A relative increase in ruptured aneurysm coiling was noted in low coiling volume hospitals of 41.1% (95% CI 32.3% to 50.6%, p=0.008) despite a decrease in SAH admissions in this tertile. INTERPRETATION: There was a relative decrease in the volume of SAH hospitalisations, aneurysmal SAH hospitalisations and ruptured aneurysm embolisations during the COVID-19 pandemic. These findings in SAH are consistent with a decrease in other emergencies, such as stroke and myocardial infarction