Search

(1) Background: Distal pancreatectomy is a standard treatment for tumours of the pancreatic body and tail. Minimally invasive techniques for all types of pancreatic tumours (benign and malignant) are being established, while concerns regarding oncological safety, cost effectiveness and learning curves are being explored with prospective studies. This paper presents our unit's data in the context of the above concerns and provides a relevant narrative review of the current literature. (2) Methods: Data were collected retrospectively between 2014 and 2021 for all adult patients who underwent elective distal pancreatectomy in our tertiary care referral HPB Unit. Data on demographics, underlying pathology, perioperative variables and post-operative complications were collected and reported using descriptive statistics. On review of the Miami guidelines, four important but less validated areas regarding distal pancreatectomy are presented in light of the current evidence; these are recent randomised controlled trials, oncological safety, cost effectiveness and learning curves in minimally invasive distal pancreatectomy (MIDP). (3) Results: 207 patients underwent distal pancreatectomy in total from 2014–2021, with 114 and 93 patients undergoing open and minimally invasive techniques, respectively. 44 patients were operated on for PDAC in the open vs. 17 in the minimally invasive group. The operative time was 212 min for the open and 248 min for the minimally invasive group. The incidence of pancreatic fistula was higher in the minimally invasive group vs. the open group (16% vs. 4%). (4) Conclusions: Our unit's data conform with the published literature, including three randomised control trials. These published studies will not only pave the way for establishing minimally invasive techniques for suitable patients, but also define their limitations and indications. Future studies will inform us about the oncological safety, cost effectiveness, overall survival and learning curves regarding patients undergoing minimally invasive distal pancreatectomy. [ABSTRACT FROM AUTHOR]

Colorectal liver metastases (CRLM) afflict a significant proportion of patients with colorectal cancer (CRC), ranging from 25% to 30% of patients throughout the course of the disease. In recent years, there has been a surge of interest in the application of near-infrared fluorescence (NIRF) imaging as an intraoperative imaging technique for liver surgery. The utilisation of NIRF-guided liver surgery, facilitated by the administration of fluorescent dye indocyanine green (ICG), has gained traction in numerous medical institutions worldwide. This innovative approach aims to enhance lesion differentiation and provide valuable guidance for surgical margins. The use of ICG, particularly in minimally invasive surgery, has the potential to improve lesion detection rates, increase the likelihood of achieving R0 resection, and enable anatomically guided resections. However, it is important to acknowledge the limitations of ICG, such as its low specificity. Consequently, there has been a growing demand for the development of tumour-specific fluorescent probes and the advancement of camera systems, which are expected to address these concerns and further refine the accuracy and reliability of intraoperative fluorescence imaging in liver surgery. While NIRF imaging has been extensively studied in patients with CRLM, it is worth noting that a significant proportion of published research has predominantly focused on the detection of hepatocellular carcinoma (HCC). In this study, we present a comprehensive literature review of the existing literature pertaining to intraoperative fluorescence imaging in minimally invasive surgery for CRLM. Moreover, our analysis places specific emphasis on the techniques employed in liver resection using ICG, with a focus on tumour detection in minimal invasive surgery (MIS). Additionally, we delve into recent developments in this field and offer insights into future perspectives for further advancements. [ABSTRACT FROM AUTHOR]

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols. [ABSTRACT FROM AUTHOR]

The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms. [ABSTRACT FROM AUTHOR]

The brute-force behaviour of High-Efficiency Video Coding (HEVC) is the biggest hurdle in the communication of the multimedia contents. Therefore, a novel method will be presented here to expedite the intra mode decision process of HEVC. In the first step, the feasibility of the odds-algorithm for the early intra mode decision is presented by using statistical evidence. Then, various elements of odds algorithm are analyzed and then mapped to the intra mode process (elements) of HEVC. Finally, the probability required by the odds algorithm is obtained by utilizing the correlation between the current and the neighboring blocks. The proposed algorithm accelerated the encoding process of the HEVC by 25% to 35%, while the Bjontegaard Delta Bit Rate (BD-BR) is 0.95% to 1.84%. [ABSTRACT FROM AUTHOR]

High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the 'German Tanks Problem' (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%. [ABSTRACT FROM AUTHOR]

Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper introduces the power-of-two (Po2) and dynamic-fixed-point (DFP) quantization techniques for weight compression and the sparsity induction in filters. Weight quantization re-training (WQR), the log-polar, and the inverse log-polar geometric transformations are introduced to reduce quantization error. WQR is a method of retraining the CPR filter, which is presented to recover the accuracy loss. It forces the given quantization scheme by adding the quantization error in the training sample and then re-quantizes the filter to the desired quantization levels which reduce quantization noise. Further, Particle Swarm Optimization (PSO) is used to fine-tune parameters during WQR. Both geometric transforms are applied as pre-processing steps. The Po2 quantization scheme showed better performance close to the performance of full precision, while the DFP quantization showed further closeness to the Receiver Operator Characteristic of full precision for the same bit-length. Overall, spatial-trained filters showed a better compression ratio for Po2 quantization after retraining of the CPR filter. The direct quantization approach achieved a compression ratio of 8 at 4.37× speedup with no accuracy degradation. In contrast, quantization with a log-polar transform is accomplished at a compression ratio of 4 at 1.12× speedup, but, in this case, 16% accuracy of degradation is noticed. Inverse log-polar transform showed a compression ratio of 16 at 8.90× speedup and 6% accuracy degradation. All the mentioned accuracies are reported for a common database. [ABSTRACT FROM AUTHOR]