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        <title>Frontiers in Medical Engineering | Clinical Engineering section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/medical-engineering/sections/clinical-engineering</link>
        <description>RSS Feed for Clinical Engineering section in the Frontiers in Medical Engineering journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-14T06:36:53.220+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2026.1774073</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2026.1774073</link>
        <title><![CDATA[Quality control in multiple-breath washout measurements: an automated, model-based approach quantifies effects on primary outcomes]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Florian Wyler</author><author>Silvio Borer</author><author>Marion Curdy</author><author>Xenia Bovermann</author><author>Bettina Sarah Frauchiger</author><author>Philipp Latzin</author>
        <description><![CDATA[Quality control (QC) is an important but challenging step in the correct interpretation of multiple-breath washout (MBW) tests, as irregular behaviors are frequent, particularly among young and ill patients, and can lead to biased outcomes. We developed a model-based, automated QC algorithm to estimate the effect of irregular behaviors on MBW outcomes. It compares results between simulated measurements containing either ideal target behaviors or the observed irregular behaviors of the measurement. Differences in primary outcomes - lung clearance index (LCI), functional residual capacity - between simulations served as behavior-specific QC outcomes. We validated the automated QC algorithm against a dataset of 2,471 measurements of 87 children with cystic fibrosis (CF). We compared the results of automated QC with the ones performed by experienced raters in a second dataset of 100 measurements of healthy children and children with CF. The test could be applied successfully to all measurements. We found that the impact of QC-related factors on MBW outcomes explained around 45% of the within-visit variability of LCI in children with CF. The largest effect on LCI was caused by leaks or trapped gas (in 31% of measurements), changes in end-expiratory lung volume (23%), and variations in breath size around the end-of-test (19%). For the second dataset, the agreement between automated QC and QC by experienced raters was moderate but comparable to QC performed by two experienced raters. Our novel automated QC tool estimates the accuracy of MBW outcomes, providing a fast, reproducible and impact-based method to perform QC for MBW measurements.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2026.1548013</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2026.1548013</link>
        <title><![CDATA[Impact energy during cadaveric transforaminal lumbar interbody fusion (TLIF) is replicated by a modular benchtop device]]></title>
        <pubdate>2026-02-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Caitlin Luke</author><author>Micah Foster</author><author>Alexis Graham</author><author>Tanner Jones</author><author>Halleigh Faulkner</author><author>Alyna-Marie Janus</author><author>Clark Hensley</author><author>Jerald Redmond</author><author>MeLeah A. Henson</author><author>Lauren B. Priddy</author><author>Matthew W. Priddy</author>
        <description><![CDATA[In the treatment of intervertebral disc pathologies, lumbar interbody fusion (LIF) involves the removal and replacement of the degenerative disc with an interbody fusion device (IFD). Leveraging a benchtop impaction device for repeatable replication of cadaveric IFD insertion would alleviate the challenges of relying solely on cadaveric models and accelerate the establishment of use condition parameters for refinement of IFD design and LIF procedures. Using a custom benchtop device, the objective of this work was to determine the benchtop testing conditions which mimic the impact energy absorption during cadaveric transforaminal lumbar interbody fusion (TLIF). From initial experiments with 1.0 lb (0.454 kg) drop weights of steel, zinc, and aluminum, we determined aluminum facilitated an impact duration closest to that of historical cadaveric TLIF data. Thus, subsequent testing utilized the aluminum drop weight impacting Ti-6Al-4V IFDs (12 mm height x 24 mm length). A lateral compressive load of 200 N was applied using springs with a stiffness of 63.22 lb/in (11.07 N/mm). Drop heights from which the aluminum weight was dropped ranged from 60 cm to 120 cm in increments of 10 cm. Data were collected by an impact force sensor on the IFD insertion instrument, button compression sensors on the compression platens, and a laser displacement sensor below the IFD. The impact waveform for benchtop and cadaveric data was characterized by four waveform characteristics: impact duration, area under the impulse curve, peak force, and initial impact slope of the waveform. Area under the curve represented energy absorption from the insertion instrument to the IFD. The cadaveric impact duration was replicated by all drop height groups on the benchtop device, and the peak impact force was replicated by the 90 cm, 110 cm, and 120 cm groups. The area under the impulse curve was replicated by the 110 cm and 120 cm groups. No benchtop group replicated the initial impact slope of the waveform seen in cadaveric data. In conclusion, a benchtop impaction device was validated for replication of energy absorption during insertion of IFDs in cadaveric TLIF procedures. Ultimately, this work will accelerate advancements in IFD design and failure analysis, enhancing the repeatability of TLIF surgical techniques.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2025.1476892</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2025.1476892</link>
        <title><![CDATA[Bio-sampling alternative diagnostic imaging for telemedicine: a feasibility study of an AI-based throat camera for influenza]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Masayoshi Nageishi</author><author>Shingo Kano</author>
        <description><![CDATA[IntroductionTelemedicine has progressed rapidly alongside advances in information and diagnostic technologies, providing benefits in accessibility, convenience, cost efficiency, and infection control. However, reliance on biological sample–based diagnostics, such as blood and urine tests, has limited the scope of fully remote diagnosis, confining many services to hybrid or in-clinic settings. Although home-based biological testing has partially addressed these constraints, fundamental barriers to complete remote diagnostic workflows remain.MethodsThis study evaluates the feasibility of bio-sampling alternative diagnostic imaging (BADI), an artificial intelligence–based diagnostic approach recently introduced in influenza testing. We developed a conceptual framework to compare conventional in-clinic testing, home-based biological testing, and BADI-based diagnostics across key dimensions relevant to remote medical implementation.ResultsThe analysis identified four essential conditions for the transition of BADI-type diagnostics to remote healthcare delivery: (1) sufficient availability and accessibility for patients, (2) assurance of diagnostic accuracy and clinical reliability, (3) high usability and interpretive clarity for both patients and healthcare providers, and (4) seamless integration into medical systems with immediate linkage to appropriate treatment.ConclusionBADI demonstrates significant potential to overcome the limitations of biological sample–dependent diagnostics in telemedicine. Addressing the identified conditions is critical for enabling safe, scalable, and effective remote diagnosis and for advancing fully remote healthcare models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1463793</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1463793</link>
        <title><![CDATA[3D scanner measuring preterm infants’ head circumference and cranial volume: validation in a simulated care setting]]></title>
        <pubdate>2024-11-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ronald van Gils</author><author>Onno Helder</author><author>René Kornelisse</author><author>Irwin Reiss</author><author>Jenny Dankelman</author>
        <description><![CDATA[IntroductionWeekly head circumference (HC) measurements using a measuring tape is the current standard for longitudinal brain growth monitoring of preterm infants. The MONITOR3D (M3D) 3D scanner has been developed to measure both HC and cranial volume (CrV) of preterm infants within incubators. The M3D’s usability, accuracy and precision were validated in a simulated setting in a neonatal intensive care unit (NICU).Materials and methodsDuring a simulated routine care moment, NICU nurses conducted M3D scans of a preterm doll simulating an extreme low birthweight preterm (ELBW; BW < 1,000 g) infant, followed by manual HC measurements using a measuring tape. Usability was quantified by percentage of successful HC and CrV measurements from scans. HC and CrV were calculated by marking anatomical landmarks on the 3D image. Measurements were compared to the real, ground truth (GT) values of the doll’s head, defined by an accurate medical scanner. Measurement accuracy was assessed using mean or median absolute measurement error (ME), and precision by the spread of ME, represented by the 95% interval of the ME range. ME intervals were compared with preterm weekly growth increases to assess clinical usability.ResultsRegarding usability, 56 M3D scan sessions resulted in 25 successful (44.6%) HC and CrV measurements, with incomplete 3D data being the primary cause of unsuccessful scans. Accuracy of the measuring tape for HC was 0.2 cm (proportional 0.9% of GT), and precision was 1.6 cm (6.3%). M3D’s accuracy of HC was 0.4 cm (1.5%), and precision was 0.7 cm (2.9%). For CrV, M3D’s accuracy was 8.0 mL (3.8%) and precision 22.6 mL (10.8%).ConclusionThe M3D scanner is suitable for measuring HC and CrV in ELBW infants. However, current scan success rate is too low for practical usability. The M3D’s accuracy and precision are clinically sufficient, while the precision of the current measuring tape method is inadequate for preterm infants. This makes the M3D a promising alternative for HC, offering less disturbance to the infant. In the future, the M3D technique could facilitate the creation of CrV growth reference charts for ELBW infants, enhancing the accuracy of clinical growth monitoring for preterm infants.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2024.1434753</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2024.1434753</link>
        <title><![CDATA[Identifying neurophysiological correlates of stress]]></title>
        <pubdate>2024-10-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dingyi Pei</author><author>Shravika Tirumala</author><author>Kyaw T. Tun</author><author>Akshara Ajendla</author><author>Ramana Vinjamuri</author>
        <description><![CDATA[Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting wellbeing and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-s neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmede.2023.1209252</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmede.2023.1209252</link>
        <title><![CDATA[Arousal detection by using ultra-short-term heart rate variability (HRV) analysis]]></title>
        <pubdate>2023-08-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mahtab Mohammadpoor Faskhodi</author><author>Mireya Fernández-Chimeno</author><author>Miquel Angel García-González</author>
        <description><![CDATA[Introduction: The study of arousal is crucial as it helps to understand the role of increased physiological and psychological activation in emotions, motivation, cognitive performance, stress responses, sleep-wake cycles, and clinical application. Recent studies have shown that arousal is commonly associated with physiological changes including heart rate variability (HRV) as indicated by RR intervals. In some applications, the analysis requires analyzing short segments of RR time series, shorter than the usual 5 min HRV. The objective of this study is to check the performance of ultra-short-term HRV indices to track changes in arousal.Method: In this study, to follow arousal changes, 31 healthy subjects were examined in both non-arousal (relaxed) and aroused states. Two states of 5 minutes each are used to measure the relaxed and arousal states. After data collection, RR time series segments were obtained randomly for each subject in arousal and relaxed states in the 30s, 60s, 120s, and 240s time windows. Next, 17 ultra-short-term HRV indices were computed for each time window for RR intervals in relaxed and aroused states.Results and Discussion: Due to the findings, novel indices such as ACI and fnQ may aid in the recognition of arousal from relaxed status. The odds of ACI being higher for the same subject during a randomly selected arousal interval than during a randomly selected relax interval are 78%, 79%, 84%, and 89% for the 30s, 60s, 120s, and 240s time windows respectively. Similarly, the odds of fnQ being higher during arousal than during a relaxed state are 79%, 81%, 84%, and 85% for the 30s, 60s, 120s, and 240s time windows respectively. Therefore, ACI and fnQ provided the best performances in intra-individual arousal detection by using ultra-short-term HRV analysis among all of the obtained indices. Nevertheless, when pooling the indices for all the subjects, the inter-subject variability causes a moderate classification performance for all indices. In this case, the best performing index is fnQ with an area under the receiver operator curve (AUC) of 75%, 77%, 79%, and 80% for the 30s, 60s, 120s, and 240s time windows respectively.]]></description>
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