Background: Numerous applications of artificial intelligence have been applied in radiological imaging ranging from computer-aided diagnosis based on machine learning to deep learning using convolutional neural networks. One of the nuclear medicine imaging tests being commonly performed today is bone scan. The use of deep learning methods through convolutional neural networks in bone scintigrams has not been fully explored. Very few studies have been published on its diagnostic capability of convolutional neural networks in assessing osseous metastasis. Objective: The aim of our study is to assess the classification performance of the pre-trained convolutional neural networks in the diagnosis of bone metastasis from whole body bone scintigrams of a local institutional dataset. Methods: Bone scintigrams from all types of cancer were retrospectively reviewed during the period 2019-2020 at the University of Perpetual Help Medical Center in Las Pinas City, Metro Manila. The study was approved by the Institutional Ethical Review Board and Technical Review Board of the medical center. Bone scan studies should be mainly for metastasis screening. The pre-processing techniques consisting of image normalization, image augmentation, data shuffling, and train-test split (testing at 30% and the rest (70%) was split 85% for training and 15% for validation) were applied to image dataset. Three pre-trained architectures (ResNet50, VGG19, DenseNet121) were applied to the processed dataset. Performance metrics such as accuracy, recall (sensitivity), precision (positive predictive value), and F1-scores were obtained. Results: A total of 570 bone scan images with dimension 220 x 646 pixel sizes in .tif file format were included in this study with 40% classified with bone metastasis while 60% were classified as without bone metastasis. DenseNet121 yielded the highest performance metrics with an accuracy rate of 83%, 76% recall, 86% precision, and 81% F1-score. ResNet50 and VGG19 had similar performance with each other across all metrics but generally lower predictive capability as compared to DenseNet121. Conclusion A bone metastasis machine learning classification study using three pre-trained convolutional neural networks was performed on a local medical center bone scan dataset via transfer learning. DenseNet121 generated the highest performance metrics with 83% accuracy, 76% recall, 86% precision and 81% F1-score. Our simulation experiments generated promising outcomes and potentially could lead to its deployment in the clinical practice of nuclear medicine physicians. The use of deep learning techniques through convolutional neural networks has the potential to improve diagnostic capability of nuclear medicine physicians using bone scans for the assessment of metastasis.
Deep Learning ; Machine Learning

X-linked dystonia-parkinsonism (XDP) is a rare, adult-onset, progressive, hereditary neurological movement disorder primarily affecting Filipino men with maternal families from Panay province of the Philippines. Medical treatment modalities currently being used have offered temporary symptomatic relief. Surgical management in the form of bilateral globus pallidi internae (Gpi) deep brain stimulation (DBS) has shown promising results and is increasingly being performed in advanced centers, as reported in international literature. Presented herein is the local experience of seven (7) retrospectively reviewed cases from February 2018 to February 2019 in a tertiary center in the Philippines with a particular focus on anesthetic management. All patients were male, from Panay, and presented with progressive dystonia and parkinsonism. All patients underwent planned bilateral, simultaneous DBS electrode, and implantable pulse generator (IPG) placement performed by a multidisciplinary team. Anesthetic management consisted of Bispectral Index (BIS) guided conscious sedation with low dose propofol and remifentanil infusions with a complete scalp nerve block (SB) at the start of the procedure then shifted to awake monitored anesthesia care during electrode placement, microelectrode recording (MER) and macro stimulation testing. All were put under general anesthesia with a supraglottic airway device during the placement of the internal pulse generator (IPG) in the infraclavicular area. All seven patients had successful localization, and insertion of the DBS electrode and discharged improved. The anesthetic management of the DBS used in these cases warrants further investigation and may lead to standardization of future practice.
Deep Brain Stimulation

No abstract available.
Upper Extremity Deep Vein Thrombosis

No abstract available.
Deep Brain Stimulation ; Dystonia ; Humans

Background: Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective: The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods: A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/ selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results: The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.
Coronary Artery Disease ; Deep Learning

OBJECTIVES

This study evaluated the accuracy of an artificial intelligence (AI) model in identifying retinal lesions, validated its performance on a Filipino population dataset, and evaluated the impact of dataset diversity on AI analysis accuracy.

This cross-sectional, analytical, institutional study analyzed standardized macula-centered fundus photos taken with the Zeiss Visucam®. The AI model’s output was compared with manual readings by trained retina specialists.

A total of 215 eyes from 109 patients were included in the study. Human graders identified 109 eyes (50.7%) with retinal abnormalities. The AI model demonstrated an overall accuracy of 73.0% (95% CI 66.6% – 78.8%) in detecting abnormal retinas, with a sensitivity of 54.1% (95% CI 44.3% – 63.7%) and specificity of 92.5% (95% CI 85.7% – 96.7%).

The availability and sources of AI training datasets can introduce biases into AI algorithms. In our dataset, racial differences in retinal morphology, such as differences in retinal pigmentation, affected the accuracy of AI image-based analysis. More diverse datasets and external validation on different populations are needed to mitigate these biases.

Background and Objectives: The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards. Methods: A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated. Results: With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices. Conclusions qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.
Tuberculosis ; Diagnostic Imaging ; Deep Learning

Recently, deep brain stimulation(DBS) has been applied in many patients to treat movement disorder. Though this new methodology is in the stage of settlement, many aspects on DBS has not been well known yet. I reviewed related articles and my experience to summarize facts on DBS.
Brain ; Deep Brain Stimulation* ; Humans ; Movement Disorders*

Deep vent DNA polymerase is purified from a strain of E. Coli that carries the Deep Vent DNA polymerase gene from pyrococcus species GB-D. The native organism was isolated from a submarine thermal vent at 2010 meters and is able to grow at temperature as high as 104oC. Deep Vent DNA polymerase is a high-fidelity thermophilic DNA polymerase. The Deep Vent DNA polymerase is derived from an integral 3'-5' proofreading exonuclease activity. Deep Vent DNA polymerase is ven more stable than Vent DNA polymerase at temperature of 95 to 100oC. Because of these advantages, we want to apply Deep Vent DNA polymerase for amplification of NS4 gene - hepatitis C virus by PCR. Our study showed that PCR conditions for Deep Vent DNA polymerase are rather different than for Taq DNA polymerase. The amplification of NS4 gene - hepatitis C virus with Deep Vent DNA polymerase is optimal with the following parameters: (1) A final concentration of MgSO4 in PCR mixture of 4 mM. (2) An annealing temperature of 57oC. (3) A final concentration of dNTPs of 200 mM.
Hepatitis C ; Deep Vent DNA polymerase

The field of functional neurosurgery has expanded in last decade to include newer indications, new devices, and new methods. This advancement has challenged anesthesia providers to adapt to these new requirements. This review aims to discuss the nuances and practical issues that are faced while administering anesthesia for deep brain stimulation surgery.
Anesthesia* ; Deep Brain Stimulation* ; Humans ; Neurosurgery