BACKGROUND: Anaesthesia machines, as moderate to high-risk medical devices intended for use on patients during surgical procedures must be safe and reliable with traceable performance every time they are used in healthcare practice. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel evidence-based method for conformity assessment testing of anaesthesia machines for post-market surveillance purposes. METHOD: The novel method is developed according to the International Organisation of Legal Metrology (OIML) guidelines and is to be used for the purpose of conformity assessment testing of anaesthesia machines with respect to their technical and metrological characteristics during PMS. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of anaesthesia machines as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of anaesthesia machines during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Introduction of fluids, medicaments and nutrients into the human body during hospitalization is fundamental for treatment and healing of patients. Fluids are introduced by means of infusion pumps while nutrients and medicaments are introduced by perfusion pumps. It is of vital importance for these devices to deliver exact amounts of the aforementioned substances as significant deviations can result in severe patient harm. Therefore it is important to effectively monitor their performance and prevent failures. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of infusion and perfusion pumps for post-market surveillance purposes. METHOD: The method was developed on the basis of metrology characteristics of the devices. In addition to the evaluation of essential safety and visual integrity of infusion and perfusion pumps, their performance in terms of delivered volumes was assessed and monitored. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of infusion and perfusion pumps as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of infusion and perfusion pumps during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Patient monitors are medical devices used to monitor vital parameters such as heart rate, respiratory rate, blood pressure, blood oxygen saturation, and body temperature during inpatient treatment. As such, patient monitors provide physicians with information necessary to adjust the treatment as well as evaluate the overall status and recovery of the patient. Measurements made by intrinsic sensors of patient monitors must be compliant and provide reliable readings in order to ensure safety and optimal quality of care to the patients. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of patient monitors in healthcare institutions for post-market surveillance purposes. METHOD: The method was developed on the basis of metrology characteristics of sensors used to monitor vital parameters observed by patient monitors and evaluation of their vital safety and performance parameters. In addition to the evaluation of essential safety and visual integrity of patient monitors, their performance in terms of accuracy of the readings is evaluated. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of patient monitors as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of patient monitors during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Premature born infants or infants born sick require immediate medical attention and decreasing the stress imposed onto their body by the environment. Infant incubators provide an enclosed environment that can be controlled to fit the needs of the infant. As such, their performance must be consistent and without significant deviations. The only manner to ensure this is by post-market surveillance (PMS) focused on evaluation of both safety and performance. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of infant incubators for post-market surveillance purposes. METHOD: The method was developed based on guidelines for devices providing measurements laid out by the International Organisation of Legal Metrology (OIML). The methodology was validated during a four year period in healthcare institutions of all levels. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of infant incubators as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of infant incubators during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Therapeutic ultrasounds are medical devices used for treatment of conditions such muscle spasms, joint contractures or general muscle pain. Their function relies in the delivery of ultrasonic pulses that generate heat in tissue thus relieving the symptoms of aforementioned conditions. Accuracy of the delivered pulses directly affects the quality and effectiveness of the treatment and has to be ensured throughout the utilization of the therapeutic ultrasound in practice. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of therapeutic ultrasounds for post-market surveillance purposes. METHOD: The method was developed based on metrology characteristics of therapeutic ultrasounds and includes visual, electrical safety and performance inspections of therapeutic ultrasounds to ensure that both safety and treatment reliability are achieved. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of therapeutic ultrasounds as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of therapeutic ultrasounds during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Defibrillators are medical devices (MDs) used in the most critical situations, hence their performance must be ensured at all times. This requires defibrillators to be subjected to regular performance assessments after they have been placed on the market. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel evidence-based method for conformity assessment testing of defibrillators. METHOD: The proposed method is developed in accordance with the International Organisation of Legal Metrology (OIML) guidelines and is intended to be used for conformity assessment testing of defibrillators for post-market surveillance purposes. RESULTS: The developed method was validated from 2018 to 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of defibrillators as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of defibrillators during PMS, besides increasing reliability of the devices, is the first step in the digital transformation of the management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Monitoring cardiac parameters is the fundamental aspect of every diagnostic process and is facilitated by electrocardiography (ECG) devices. This way, continuous state-of-the-art performance of ECG devices can be ensured. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of ECG devices for post-market surveillance purposes. METHOD: The method was developed on the basis of International Organisation of Legal Metrology (OIML) guidelines and applied in healthcare institutions from 2018 to 2021. RESULTS: The developed method was validated in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of the ECG device as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of ECG devices during PMS, besides increasing reliability of the devices, is the first step in the digital transformation of the management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

BACKGROUND: Medical devices (MDs) represent the backbone of the modern healthcare system. Considering their importance in daily medical practice, the process of manufacturing, marketing and usage has to be regulated at all levels. Harmonized evidence-based conformity assessment of MDs during PMS relying on traceability of medical device measurements can contribute to higher reliability of MD performance and consequently to higher reliability of diagnosis and treatments. OBJECTIVE: This paper discusses issues within MD post-market surveillance (PMS) mechanisms in order to set a path to harmonization of MD PMS. METHODS: Medline (1980–2021), EBSCO (1991–2021), and PubMed (1980–2021) as well as national and international legislation and standard databases along with reference lists of eligible articles and guidelines of relevant regulatory authorities such as the European Commission and the Food and Drug Administration were searched for relevant information. Journal articles that contain information regarding PMS methodologies concerning stand-alone medical devices and relevant national and international legislation, standards and guidelines concerning the topic were included in the review. RESULTS: The search strategy resulted in 2282 papers. Out of those only 24 articles satisfied the eligibility criteria and were finally included in the review. Papers were grouped per categories: medical device registry, medical device adverse event reporting, and medical device performance evaluation. In addition to journal articles, national and international legislation, standards, and guidelines were reviewed to assess the state of PMS in different regions of the world. CONCLUSION: Although the regulatory framework prescribes PMS of medical devices, the process itself is not harmonized with international standards. Particularly, conformity assessment of MDs, as an important part of PMS, is not measured and managed in a traceable, evidence-based manner. The lack of harmonization within PMS results in an environment of increased adverse events involving MDs and overall mistrust in medical device diagnosis and treatment results.

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus. Diagnosis of the disease itself is difficult because the incubation period is long, often the disease is initially without some characteristic symptoms, but also due to a lack of laboratory methods. Artificial intelligence is increasingly being used nowadays to make it easier and faster to assess the illness. As hepatitis C is a rising healthcare burden it is of utmost importance to construct effective and reliable screening methods. As AI has already proven useful for diagnosis of a variety of conditions based on clinical parameters, this study focuses on the application of artificial neural network (ANN) for hepatitis C diagnosis. In this study, a database of 1000 respondents divided into two groups was used to develop the ANN: healthy (n = 200) and sick (n = 800). Monitoring parameters were: albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, bilirubin, acetylcholinesterase and anti-HCV antibodies. The overall accuracy of the developed ANN was 97,78%, which indicates that the potential of artificial intelligence in diagnosing hepatitis C is enormous, and in the future, attention should be paid to the development of new systems with as much data as possible.

The primary focus of this paper review is to summarize the most important facts and findings regarding the use of Artificial Intelligence (AI) in the modeling, processing and analysis of biomedical data and to give an insight on the contributions of AI, Machine learning and Deep learning to the field of medicine. This study compiled and analyzed work published in the period between 1986 and 2021 related to the use of AI in medicine, its various applications and historical development, with a focus on papers published from 2015 until today, due to the accumulation and development of newer technologies. Out of a total of 117 papers reviewed, 52 were selected for a more detailed analysis and presented in a table summarizing the key points, advances, advantages and disadvantages of AI, its subfields and algorithms. The goal of this paper was to extract the most famous AI learning algorithms, past and current, and focus on the methods of modeling, processing and analysis by which these algorithms operate and perform tasks in order to help doctors and experts better understand the underlying mechanisms behind biological processes, and in some cases, even replace humans in data classification, identification, diagnosis and prediction of different conditions associated with diseases.

While examining biomedical signals, signal classification as well as measurements, quantifications and their assessment is very important for studying different diseases and disorders. Through this paper, we have focused on different signals and biomedical devices, whose purpose is to give high quality information about diseases and disorders in prenatal age. The main focus was on ultrasound techniques and the relationship between 2D, 3D and 4D ultrasound, on Doppler ultrasound, cardiotocography, KANET test, and in general, comparison of standardized and automated techniques. Purpose of this paper is to compare some of the available techniques used to assess the fetus in the womb, how they advance through time and whether they are being automated.

Pulmonary emphysema is a complicated disease caused by irreversible damage to the wall of the pulmonary alveoli and causes 5% of the total mortality worldwide. This paper presents the development of an artificial neural network (ANN) for the diagnosis of pulmonary emphysema. Following biomarkers were used for the development of the ANN: AAT (alphal-antitrypsin), FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity) and FEV1/FVC (ratio forced expiratory volume in 1 second / forced vital capacity). The dataset consisted of 300 patients: 210 healthy subjects and 90 subject with disease. The neural network has 4 input parameters and 1 output parameter. For the final architecture, a neural network with 13 neurons in hidden layer was chosen based on the training results. The developed ANN has shown good performance and has a potential for use in this field.

As a consequence of the progress of the modern mobile medicine, wearable technologies, especially ECG wearables tend to become indispensable part of peoples' lives. As applications and devices for tracking cardiac electrical activity are rapidly entering the market, it is important to compare individual ECG wearable devices. This review takes a systematic approach on the analysis of wearable ECG devices. It provides a detailed introduction on the updated methods, to create a comparison between individual features of devices, and to evaluate techniques for fall risk assessment, diagnosis, and prevention. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) instructions were used as a report standard. In an effort to collect the appropriate data, various databases were queried together with specific subject-oriented keywords. This was combined with different inclusion and exclusion criteria to find the relevant data. To further improve the data gathering and reduce bias, a Zotero tool was used. The results of this paper show the comparison of the different devices and their features. All findings can be observed in the table and in words. As information for the QardioCore are scarce, all six authors consolidated on the VitalCore being the most accurate ECG wearable device, as its sensitivity and specificity are the highest. Recent advances in wearable ECG devices allow for more trouble free out of clinic fall risk assessment, detection and prevention. As people tend to prefer the comfort of their home over doctors, such progress will assure the everyday emerging of new wearables.