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  • 1
    Online Resource
    Online Resource
    Artifact Detection in Lung Ultrasound: An Analytical Approach
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 7 ( 2023-03-25), p. 1551-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 7 ( 2023-03-25), p. 1551-
    Abstract: Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12071551
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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  • 2
    Online Resource
    Online Resource
    Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?—Systematic Review
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 18 ( 2023-09-19), p. 2995-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 18 ( 2023-09-19), p. 2995-
    Abstract: Background: Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the evaluation of LUS videos by artificial intelligence could improve the implementation of LUS in thoracic surgery. Methods: A literature review of the replacement of the CXR by LUS after thoracic surgery and the evaluation of LUS videos by artificial intelligence after thoracic surgery was conducted in Medline. Results: Here, eight out of 10 reviewed studies evaluating LUS in CXR reduction showed that LUS can reduce CXR without a negative impact on patient outcome after thoracic surgery. No studies on the evaluation of LUS signs by artificial intelligence after thoracic surgery were found. Conclusion: LUS can reduce CXR after thoracic surgery. We presume that artificial intelligence could help increase the LUS accuracy, objectify the LUS findings, shorten the learning curve, and decrease the number of inconclusive results. To confirm this assumption, clinical trials are necessary. This research is funded by the Slovak Research and Development Agency, grant number APVV 20-0232.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13182995
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 3
    Online Resource
    Online Resource
    Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 15 ( 2021-07-29), p. 6976-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 15 ( 2021-07-29), p. 6976-
    Abstract: Certain post-thoracic surgery complications are monitored in a standard manner using methods that employ ionising radiation. A need to automatise the diagnostic procedure has now arisen following the clinical trial of a novel lung ultrasound examination procedure that can replace X-rays. Deep learning was used as a powerful tool for lung ultrasound analysis. We present a novel deep-learning method, automated M-mode classification, to detect the absence of lung sliding motion in lung ultrasound. Automated M-mode classification leverages semantic segmentation to select 2D slices across the temporal dimension of the video recording. These 2D slices are the input for a convolutional neural network, and the output of the neural network indicates the presence or absence of lung sliding in the given time slot. We aggregate the partial predictions over the entire video recording to determine whether the subject has developed post-surgery complications. With a 64-frame version of this architecture, we detected lung sliding on average with a balanced accuracy of 89%, sensitivity of 82%, and specificity of 92%. Automated M-mode classification is suitable for lung sliding detection from clinical lung ultrasound videos. Furthermore, in lung ultrasound videos, we recommend using time windows between 0.53 and 2.13 s for the classification of lung sliding motion followed by aggregation.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app11156976
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 4
    Online Resource
    Online Resource
    Prognostic Value of Sarcopenia in Metastatic Colorectal Cancer Patients Treated with Trifluridine/Tipiracil
    MDPI AG ; 2021
    In:  Journal of Clinical Medicine Vol. 10, No. 21 ( 2021-10-30), p. 5107-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 10, No. 21 ( 2021-10-30), p. 5107-
    Abstract: Sarcopenia is common in metastatic colorectal cancer (mCRC), increases the risk of treatment-related toxicity and reduces survival. Trifluridine/tipiracil (TT) chemotherapy significantly improved survival in refractory mCRC patients, but the prognostic and predictive role of pretherapeutic sarcopenia and variation in the skeletal muscle index (SMI) during this treatment has not been investigated so far. In this retrospective, observational study, clinical data on mCRC patients treated with TT at six cancer centres in Poland were collected. Computed tomography (CT) scans acquired at the time of initiation of TT (CT1) and on the first restaging (CT2), were evaluated. SMI was assessed based on the skeletal muscle area (SMA) at the level of the third lumbar vertebra. Progression-free survival (PFS) and overall survival (OS) were calculated from the treatment start. Neither initial sarcopenia nor ≥5% skeletal mass loss (SML) between CT1 and CT2 had a significant effect on PFS in treated patients (p = 0.5526 and p = 0.1092, respectively). In the multivariate analysis, reduced OS was found in patients with ≥5% SML (HR: 2.03 (1.11–3.72), p = 0.0039). We describe the prognostic role of sarcopenia beyond second line treatment and analyze other factors, such as performance status, tumor histological differentiation or carcinoembryonic antigen level that could predict TT treatment response.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm10215107
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662592-1
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  • 5
    Online Resource
    Online Resource
    In Comparison to Pathological Q Waves, Selvester Score Is a Superior Diagnostic Indicator of Increased Long-Term Mortality Risk in ST Elevation Myocardial Infarction Patients Treated with Primary Coronary Intervention
    MDPI AG ; 2021
    In:  Diagnostics Vol. 11, No. 5 ( 2021-04-28), p. 799-
    Details
    In: Diagnostics, MDPI AG, Vol. 11, No. 5 ( 2021-04-28), p. 799-
    Abstract: The development of pathological Q waves has long been correlated with worsened outcome in patients with ST elevation myocardial infarction (STEMI). In this study, we investigated long-term mortality of STEMI patients treated by primary percutaneous coronary intervention (PPCI) and compared predictive values of Q waves and of Selvester score for infarct volume estimation. Data of 283 consecutive STEMI patients (103 females) treated by PPCI were analysed. The presence of pathological Q wave was evaluated in pre-discharge electrocardiograms (ECGs) recorded ≥72 h after the chest pain onset (72 h Q). The Selvester score was evaluated in acute ECGs (acute Selvester score) and in the pre-discharge ECGs (72 h Selvester score). The results were related to total mortality and to clinical and laboratory variables. A 72 h Q presence and 72 h Selvester score ≥6 was observed in 184 (65.02%) and 143 (50.53%) patients, respectively. During a follow-up of 5.69 ± 0.66 years, 36 (12.7%) patients died. Multivariably, 72 h Selvester score ≥6 was a strong independent predictor of death, while a predictive value of the 72 h Q wave was absent. In high-risk subpopulations defined by clinical and laboratory variables, the differences in total mortality were highly significant (p 〈 0.01 for all subgroups) when stratified by 72 h Selvester score ≥6. On the contrary, the additional risk-prediction by 72 h Q presence was either absent or only borderline. In contemporarily treated STEMI patients, Selvester score is a strong independent predictor of long-term all-cause mortality. On the contrary, the prognostic value of Q-wave presence appears limited in contemporarily treated STEMI patients.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics11050799
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662336-5
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  • 6
    Online Resource
    Online Resource
    Heart Rate Influence on the QT Variability Risk Factors
    MDPI AG ; 2020
    In:  Diagnostics Vol. 10, No. 12 ( 2020-12-16), p. 1096-
    Details
    In: Diagnostics, MDPI AG, Vol. 10, No. 12 ( 2020-12-16), p. 1096-
    Abstract: QT interval variability, mostly expressed by QT variability index (QTVi), has repeatedly been used in risk diagnostics. Physiologic correlates of QT variability expressions have been little researched especially when measured in short 10-second electrocardiograms (ECGs). This study investigated different QT variability indices, including QTVi and the standard deviation of QT interval durations (SDQT) in 657,287 10-second ECGs recorded in 523 healthy subjects (259 females). The indices were related to the underlying heart rate and to the 10-second standard deviation of RR intervals (SDRR). The analyses showed that both QTVi and SDQT (as well as other QT variability indices) were highly statistically significantly (p 〈 0.00001) influenced by heart rate and that QTVi showed poor intra-subject reproducibility (coefficient of variance approaching 200%). Furthermore, sequential analysis of regression variance showed that SDQT was more strongly related to the underlying heart rate than to SDRR, and that QTVi was influenced by the underlying heart rate and SDRR more strongly than by SDQT (p 〈 0.00001 for these comparisons of regression dependency). The study concludes that instead of QTVi, simpler expressions of QT interval variability, such as SDQT, appear preferable for future applications especially if multivariable combination with the underlying heart rate is used.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics10121096
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662336-5
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