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An Identification of Handling Uncertainties Within Medical Screening: A Case Study Within Screening for Breast Cancer
Published in Horia-Nicolai Teodorescu, Abraham Kandel, Lakhmi C. Jain, FUZZY and NEURO-FUZZY SYSTEMS in MEDICINE, 2017
Fredrik Georgsson, Patrik Eklund
Auscultation and Percussion. Auscultation may be used to diagnose early heart diseases along with percussion. Auscultation is when a physician listens to the patient with a stethoscope, or some other sound-amplifying device. For percussion, the physician listens to the sounds generated in the patient after a mechanical influence [17]. These two methods are completely harmless to the patient, but must be performed by a physician.
From surface realism to training considerations: a proposal for changing the focus in the design of training systems
Published in Theoretical Issues in Ergonomics Science, 2020
In addition to the potentials of using low-fidelity training materials for declarative and procedural tasks, the medical skills that depend on sensory recognition can also benefit from low-fidelity training. As an example, recognising the regularity of heart sounds and classifying types of irregularities – i.e. auscultation – is an important clinical skill. To train this skill, Harvey® – a $75,000 high-fidelity and life-sized mankin – has been used for decades. In a study by de Giovanni, Roberts, and Norman (2009), 37 medical students were divided between two groups: one received training materials of heart sounds with Harvey® and the other group was trained using recorded sounds via a CD player. Following a six-week interval, trainees’ performance in diagnostic accuracy and clinical skills was tested on real patients, and there was no significant difference in clinical or detection skills between the two groups. These findings open the possibility of breaking expensive and complex whole-task trainers (e.g. Harvey®) into smaller parts, and train each part as a separate task with an inexpensive trainer (e.g. CD player) – that was the idea behind part-task training.
Towards classifying non-segmented heart sound records using instantaneous frequency based features
Published in Journal of Medical Engineering & Technology, 2019
The employment of computerised analysis and diagnosis in the medical field has enhanced dramatically over the last decades especially with the advancement of signal processing, image processing, and machine learning techniques. Using computer software in health care facilities as an aided-diagnostic tool provides great and important help. Computer-aided diagnosis is very helpful, especially when there is an essential need for early diagnosis [1,2]. One of the most recent and important examples on which the computerised analyses are required is the digital stethoscope based on auscultation technique used to provide doctors with the accurate and informative interpretation of heart sounds. Heart sounds are generated by heart valves opening and closing and detected by using stethoscopes and can be saved to a computer for further processing [3].
Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation
Published in Journal of Medical Engineering & Technology, 2023
Youness Arjoune, Trong N. Nguyen, Robin W. Doroshow, Raj Shekhar
Electronic stethoscopes, with expanded memory, have expedited building cardiopulmonary disease libraries for teaching auscultation [6–8]. Rare heart sounds could thus be easily shared, providing practical offline training. As more such libraries have become accessible to medical practitioners, one would expect that auscultation skills would improve, but many studies have reported the opposite [2,9–13]. In response, computer-aided auscultation is emerging to support this critical diagnostic skill [13], and researchers are using various electronic stethoscopes to collect cardiopulmonary data and train artificial intelligence (AI) models (Table 1).