Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis.
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ABSTRACT:
SUBMITTER: Nakhle F
PROVIDER: S-EPMC8441561 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.
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Project description:In the past, the skills required to make an accurate dermatological diagnosis have required exposure to thousands of patients over many years. However, in recent years, artificial intelligence (AI) has made enormous advances, particularly in the area of image classification. This has led computer scientists to apply these techniques to develop algorithms that are able to recognize skin lesions, particularly melanoma. Since 2017, there have been numerous studies assessing the accuracy of algorithms, with some reporting that the accuracy matches or surpasses that of a dermatologist. While the principles underlying these methods are relatively straightforward, it can be challenging for the practising dermatologist to make sense of a plethora of unfamiliar terms in this domain. Here we explain the concepts of AI, machine learning, neural networks and deep learning, and explore the principles of how these tasks are accomplished. We critically evaluate the studies that have assessed the efficacy of these methods and discuss limitations and potential ethical issues. The burden of skin cancer is growing within the Western world, with major implications for both population skin health and the provision of dermatology services. AI has the potential to assist in the diagnosis of skin lesions and may have particular value at the interface between primary and secondary care. The emerging technology represents an exciting opportunity for dermatologists, who are the individuals best informed to explore the utility of this powerful novel diagnostic tool, and facilitate its safe and ethical implementation within healthcare systems.
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Project description:Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-to-end learning workflows are introduced to produce three-dimensional (3D) information in form of digital surface and terrain models, textured meshes, rectified mosaics, CAD models, etc. These 3D products might be used in applications where accuracy and precision play a vital role, e.g. structural health monitoring. Therefore, extensive tests against data with relevant characteristics and reliable ground-truth are required to assess and ensure the performance of 3D modelling workflows. This article describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine accompanied with additional data that will allow implementing and evaluating any structure-from-motion or photogrammetric approach for sparse or dense 3D reconstruction. This dataset includes total of 158 high-quality images captured with more than 80% endlap and spatial resolution higher than 1.5 cm, the 3D coordinates of 109 ground control points and checkpoints, 2D coordinates of more than 40K corresponding points among the images, a subset of 25 multi-view stereo images selected from an area of approximately 30 m × 40 m within the scene accompanied with a dense point cloud measured by a terrestrial laser scanner.
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Project description:BACKGROUND:Bioinformatics has pervaded all fields of biology and has become an indispensable tool for almost all research projects. Although teaching bioinformatics has been incorporated in all traditional life science curricula, practical hands-on experiences in tight combination with wet-lab experiments are needed to motivate students. RESULTS:We present a tutorial that starts from a practical problem: finding novel enzymes from marine environments. First, we introduce the idea of metagenomics, a recent approach that extends biotechnology to non-culturable microbes. We presuppose that a probe for the screening of metagenomic cosmid library is needed. The students start from the chemical structure of the substrate that should be acted on by the novel enzyme and end with the sequence of the probe. To attain their goal, they discover databases such as BRENDA and programs such as BLAST and Clustal Omega. Students' answers to a satisfaction questionnaire show that a multistep tutorial integrated into a research wet-lab project is preferable to conventional lectures illustrating bioinformatics tools. CONCLUSION:Experimental biologists can better operate basic bioinformatics if a problem-solving approach is chosen.
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