Phakic iris-claw intraocular lens: calculations of power and long-term endothelial cells loss.

A correction about phakic intraocular lens power calculation process, as it was stated by Li, Song & Song, is provided. It is explained that this calculation is based on the Van der Heijde formula and not on biometric formulas. A comment about the mechanisms of late endothelial cell loss following phakic intraocular lenses is done.

Coherence Analysis between the Surface Diaphragm EMG Envelope Signal and the Respiratory Signal derived from the ECG in Patients assisted by Mechanical Ventilation.

Prolonged use of mechanical ventilation (MV) can lead to greater complications for a patient. In clinical practice, it is important to identify patients who could fail in the extubation process. However, accurately predicting the outcome of this process remains a challenge. The diaphragm muscle is one of the most active elements in the breathing process. On the other hand, there are several techniques to derive respiratory information from the ECG signal. Signals derived from diaphragmatic activity and from the ECG, such as the envelope of the surface diaphragm electromyographic signal (sEMGi) and the respiratory signal derived from the electrocardiogram (ECG) could contribute to analyze the respiratory response in patients assisted by MV. This work proposes the analysis of the coherence between sEMGi and EDR signals to determine possible differences in the respiratory pattern between successful and failed patients undergoing weaning. 40 patients with MV, candidates for weaning trial process and underwent a spontaneous breathing test were analyzed, classified into: a successful group (SG: 19 patients) that maintained spontaneous breathing after the test, and a failed group (FG: 21 patients) that required reconnection to the MV. The cross correlation, power spectral density and magnitude squared coherence (MSC) of the sEMGi and the EDR signals were estimated. According to the results, the MSC parameters such as area under the curve and mean coherence value presented statistically significance differences between the two groups of patients (p = 0.024). Our results suggest that both sEMGi and EDR signals could provide information about the behavior of the respiratory system in these patients. Clinical Relevance- This study analyzes the correlation and the coherence between the envelope of the surface electromyographic signal and the respiratory signal derived from the ECG to characterize the respiratory pattern of successful and failed patients on weaning process.

A new protocol to compare successful versus failed patients using the electromyographic diaphragm signal in extubation process.

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance-This establishes the characterization of success and failure patients in the extubation process.

Artificial Neural Network for Laparoscopic Skills Classification Using Motion Signals from Apple Watch.

The acquisition of laparoscopic technical skills is constrained by the limited training opportunities and the necessity of having staff physicians on site to provide feedback to the trainees. In addition, the assessment tools used to measure trainees performance are not always sensitive enough to detect different levels of expertise. To address this problem, two Apple Watches worn on inexperienced subjects in laparoscopy were used to record their motion signals (attitude, rotation rate and acceleration) during multiple practices of the peg transfer task in a fundamentals of laparoscopic surgery (FLS) trainer box. This training process was carried out through a massed practice methodology (two hours of training), in which subjects were assessed following the guidelines of the FLS program. Subsequently, a series of metrics were estimated from the acquired motion signals and the Spearman's rank correlation coefficient was used to select the most statistically significant attributes. Then, a classification model based on artificial neural networks was trained, using these attributes as model inputs, to classify trainees according to their level of expertise into three classes: low, intermediate and high. Using this approach, an average classification performance of F1=86.11% was achieved on a test subset. This suggests that new technologies, such as smartwatches, can be used to complement surgical training by including motion-based metrics to improve current clinical education and offering a new source of feedback through objective assessment.

Atrial Fibrillation Detection in Short Single Lead ECG Recordings Using Wavelet Transform and Artificial Neural Networks.

Atrial fibrillation (AF) is a common health issue, not only in developed countries but also in developing ones. AF can lead to strokes, heart failures, and even death if it is not diagnosed and treated on time, therefore automatic detection of AF is an urgent need, particularly using Internet- connected devices that can alert healthcare services. Detection of AF typically involves the analysis of electrocardiogram (ECG) recordings, where P-waves that characterize the atrial activity are substituted with f-waves of variable amplitude and duration. In this paper, we used the discrete wavelet transform to decompose the ECG signal into detail and approximation coefficients with different time-frequency resolutions. Features extracted from ECG signals, RR interval time series and detail and approximation coefficients were used as inputs to an artificial neural network trained to identify four classes of heart rhythms: normal sinus rhythm (NSR), AF, other rhythms (OR) and noisy signals (NS). By performing a Monte Carlo 10- fold cross-validation of 10 iterations approach, average micro F1 scores of 83.64%, 61.61%, 56.88% and 53.88% to classify NSR, AF, OR and NS respectively, and average macro F1 of 64.00% were obtained on the publicly available training set of PhysioNet/Computing in Cardiology Challenge 2017. In addition, in a one-vs.-the-rest strategy, i.e., AF-vs-the-rest, averages sensitivity and specificity of 95.70% and 72.39% respectively were achieved to classify AF recordings.

Proposal for a New Training Exercise for Single Port Laparoscopic Cholecystectomy.

This paper presents the design and implementation of a new training exercise to improve technical textbfskills in the surgeons who performtextbf Single Port Laparoscopic Cholecystectomy (SPLC), a technique that requires active improvements to overcome the lack of triangulation and collision of instruments both within and outside the abdominal cavity. The proposed mechanisms were developed based on peg transfer tests, performed by an expert surgeon in SPLC, with straight forceps and SILS$^{\mathbf {TM}}$ access in a pelvictrainer afterwards an unstructured interview was showed to the surgeon. These methodological tools provided the characteristics of the type of movement required by the peg transfer test at the time of performing the training task, which was taken as a reference to propose a new protocol to be implemented. The mentioned structure was tested by an expert surgeon, who performed 5 tasks, within an average running time of 170 seconds. At the end of the tests, a semi-structured interview was carried out again to the surgeon, where the improvement of the technique was tested.