Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and longterm risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. Methods:The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results:Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean 𝐿 1 difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (𝐶𝐼 95 ) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions. Conclusions:The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.
This work describes the design of a new mobile health (mHealth) platform for a continuous real time remote patient monitoring named C-SMART. The platform is based on a set of sensors for patient's physiological condition assessment, a mobile phone, and a centralized healthcare utility. C-SMART is implemented on application layer and thus can be compatible to different existing telemedicine and medical data base standards in particular to IEEE 11073. A major concern in the design of the system is given to exploit existing hardware and software resources and thus reduce the platform overhead with minimal user intervention and minimal cost. Another main concern in the design is to make the platform working in a plug and play manner, but yet to give the user maximum control on the system operation. It is enabled by forming a dedicated remote control and installation center and by using an operation menu at the mobile phone. A feasibility test to the platform demonstrated human activity monitoring through a standard mobile phone and a set of accelerometers, and programming of the sensors through the mobile phone.
PURPOSE: Non-small cell lung cancer (NSCLC), the most prevalent subtype of lung cancer, tends to metastasize to the brain. Between 10-60% of NSCLCs harbor an activating mutation in the epidermal growth factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular pro le of the primary tumor and the brain metastases (BMs), identifying an individual patient's EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM. METHODS:We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2006-2019. The study population was then divided into 2 groups based upon EGFR mutational status. We further employed a DL technique to classify the 2 groups according to their preoperative magnetic resonance imaging features. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC.RESULTS: Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, speci city of 97.7%, and a receiver operating characteristic curve )ROC( value of 0.91 across the 5 validation datasets.CONCLUSION: DL based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.
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