Automated image analysis systems to quantify physical and behavioral attributes of biological entities

All life forms in nature have physical and behavioral attributes which help them survive and thrive in their environment. Technologies, both within the areas of hardware systems and data processing algorithms, have been developed to extract relevant information about these attributes. Understanding the complex interplay of physical and behavioral attributes is proving important towards identifying the phenotypic traits displayed by organisms. This thesis attempts to leverage the unique advantages of portable/mobile hardware systems and data processing algorithms for applications in three areas of bioengineering: skin cancer diagnostics, plant parasitic nematology, and neglected tropical disease. Chapter 1 discusses the challenges in developing image processing systems that meet the requirements of low cost, portability, high-throughput, and accuracy. The research motivation is inspired by these challenges within the areas of bioengineering that are still elusive to the technological advancements in hardware electronics and data processing algorithms. A literature review is provided on existing image analysis systems that highlight the limitations of current methods and provide scope for improvement. Chapter 2 is related to the area of skin cancer diagnostics where a novel smartphone-based method is presented for the early detection of melanoma in the comfort of a home setting. A smartphone application is developed along with imaging accessories to capture images of skin lesions and classify them as benign or cancerous. Information is extracted about the physical attributes of a skin lesion such as asymmetry, border irregularity, number of colors, and diameter. Machine learning is employed to train the smartphone application using both dermoscopic and digital lesion images. Chapter 3 is related to the area of plant parasitic nematology where automated methods are presented to provide the nematode egg count from soil samples. A new lensless imaging system is built to record holographic videos of soil particles flowing through microscale flow assays. Software algorithms are written to automatically identify the nematode eggs from low resolution holographic videos or images captured from a scanner. Deep learning algorithm was incorporated to improve the learning process and train the software model. Chapter 4 is related to the area of neglected tropical diseases where new worm tracking systems have been developed to characterize the phenotypic traits of Brugia malayi adult male worms and their microfilaria. The worm tracking algorithm recognizes behavioral attributes of these parasites by extracting a number of features related to their movement and body posture. An imaging platform is optimized to capture high-resolution videos with appropriate field of view of B. malayi. The relevance of each behavioral feature was evaluated through drug screening using three common antifilarial compounds. The abovementioned image analysis systems provide unique advantages to the current experimental methods. For example, the smartphone-based software application is a low-cost alternative to skin cancer diagnostics compared to standard dermoscopy available in skin clinics. The lensless imaging system is a low-cost and high-throughput alternative for obtaining egg count densities of plant parasitic nematodes compared with visual counting under a microscope by trained personnel. The B. malayi worm tracking system provides an alternative to available C. elegans tracking software with options to extract multiple parameters related to its body skeleton and posture

A privacy preserving online learning framework for medical diagnosis applications

Electronic Health records are an important part of a digital healthcare system. Due to their significance, electronic health records have become a major target for hackers, and hospitals/clinics prefer to keep the records at local sites protected by adequate security measures. This introduces challenges in sharing health records. Sharing health records however, is critical in building an accurate online diagnosis framework. Most local sites have small data sets, and machine learning models developed locally based on small data sets, do not have knowledge about other data sets and learning models used at other sites. The work in this thesis utilizes the framework of coordinating the blockchain technology and online training mechanism in order to address the concerns of privacy and security in a methodical manner. Specifically, it integrates online learning with a permissioned blockchain network, using transaction metadata to broadcast a part of models while keeping patient health information private. This framework can treat different types of machine learning models using the same distributed dataset. The study also outlines the advantages and drawbacks of using blockchain technology to tackle the privacy-preserving predictive modeling problem and to improve interoperability amongst institutions. This study implements the proposed solutions for skin cancer diagnosis as a representative case and shows promising results in preserving security and providing high detection accuracy. The experimentation was done on ISIC dataset, and the results were 98.57, 99.13, 99.17 and 97,18 in terms of precision, accuracy, F1-score and recall, respectively

Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

Human-computer collaboration for skin cancer recognition

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice

Federated Learning for Medical Image Analysis: A Survey

Machine learning in medical imaging often faces a fundamental dilemma, namely the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/datasets to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We first introduce the background and motivation of federated learning for dealing with privacy protection and collaborative learning issues in medical imaging. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges and potential research opportunities in this promising research field.Comment: 19 pages, 6 figure

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Mobile teledermoscopy for patients at high risk of cutaneous melanoma: A single-arm, feasibility study of a clinical intervention at two tertiary centres (MOBILEMEL study)

Mobile teledermoscopy provides faster patient-to-specialist access. We studied the feasibility of its implementation in high-risk melanoma patients. In a prospective dual-cohort study, 75 high-risk patients from two Australian tertiary centres were given phone-compatible teledermoscopes. Cohort 1 provided transmissions for lesions initiated by the (i) patient’s concern and/or (ii) dermatologist’s recommendation for sequential digital dermoscopy imaging (SDDI) over 12 months of participation. Paired data collected before (conventional face-to-face practice, FTF) and after (1 year) the teledermoscopy implementation was compared. The primary outcome was the earlier detection of skin cancers. Secondary outcomes included transmission quality of mobile teledermoscopy and associations with clinical practice, cost, and level of acceptance. Cohort 2 performed transmissions for SDDI to enhance the above data. Mobile teledermoscopy used by 75 participants (Cohort 1, n=45; Cohort 2, n=30) reduced the time-to-treatment by 50 days (p=0.039). There were 302 intended transmissions: lesion of concern comprised 22%(67/302), and SDDI comprised 78%(235/302). One-fifth of the latter (43/235;18%) were not transmitted – half either converted to visits by participants or not attempted despite reminders. Of the actual transmissions – 86%(259/302) of intended transmissions – evaluable transmissions comprised 78%(201/259). Participants >40 years old (p=0.014) or with tertiary education (p=0.015) provided more reliable transmissions. Mobile teledermoscopy had 89% diagnostic accuracy and treatment concordance with FTF visits. Visits were averted (39%) or fast-tracked (37%). Most(16/19) melanomas were identified at FTF visits. Mobile teledermoscopy had a $28/patient/year increment to the healthcare system but participants found it highly acceptable (mean confidence level 4.2/5). Earlier skin cancer treatment can occur with mobile teledermoscopy when used to support conventional practice

Digital dermatology in general practice:Past, present and future

General practitioners (GPs) serve as gatekeepers for patients seeking specialized dermatology care and play a crucial role in triaging patients with skin lesions. To support GPs in diagnosing these (suspicious) skin lesions in general practice, they can seek the advice of a teledermatologist through digital dermatology services. This thesis aimed to contribute to understand the value of store-and-forward digital dermatology consultation in Dutch general practice. Furthermore, this thesis aimed to provide insights into experienced facilitators and barriers in the uptake of these digital dermatology services by GPs since its introduction in primary care. First, we investigated the status of two decades of teledermatology worldwide by performing a literature review (Part I). Second, we researched the impact and added value of performing store-and-forward teledermoscopy for GPs in Dutch GP practice (Part II). Finally, we developed and validated a quality feedback tool (SAF-TSUQ) to determine GPs’ perspectives about store-and-forward telemedicine services. We applied and extended this SAF-TSUQ to reveal the factors that facilitate or impede the successful implementation and use of teledermatology, teledermoscopy and dermatology home consultation services in Dutch GP practice (Part III). The evidence from this thesis showed that teledermatology and teledermoscopy are of added value for GPs. These services save cost and time compared to traditional dermatology care and support GPs in their referral decisions for benign and malignant skin lesions. Barriers hinder the full potential of digital dermatology services and addressing these sociotechnical challenges is crucial for enhancing and expanding these services in future general practices