A Smarter Health through the Internet of Surgical Things

Sign of Neck Mass as the Chief Complaint: A Case Report and Literature Review About Thyroid Metastasis of Colorectal Cancer

Background

Commonly, the thyroid gland is regarded as an organ with fewer metastatic diseases, and colorectal metastasis to the thyroid (CMT) is rarely reported, especially, with that the clinical sign of thyroid metastasis nidus is the chief complaint. The CMT occurs in advanced colorectal cancer and is associated with poor prognosis and short survival.

Case Report

In this case, we reported a patient with the sign of neck mass as the first manifestation of CMT. The patient underwent a partial thyroidectomy in June 2019, immunohistochemical findings of thyroid carcinoma suggested the possibility of adenocarcinoma of gastrointestinal tract. The patient underwent a colonoscopy in July 2019 and a colonic mass was found. Pathological examination diagnosed rectal adenocarcinoma. The patient underwent neoadjuvant chemotherapy, surgical treatment, postoperative adjuvant chemotherapy and targeted therapy. The patient died in June 2022.

Conclusion

The metastasis disease would not be ignored at all, when a patient complains at signs of neck mass. Further, the possibility of metastasis cancer should be considered once thyroid nodules occur in patients with colorectal cancer. Even though the biological characteristics and stage of the primary tumor have an important impact on the prognosis, positive standardized treatments can also be helpful.

Determinants of survival and recurrence in patients with stage I colorectal cancer

Background

Due to the novel advanced screening methods, the number of patients diagnosed with stage I colorectal cancer (CRC) is increasing. This retrospective cohort study aimed to identify recurrence and survival risk factors of patients with stage I CRC after surgery.

Materials and methods

Patients with stage I CRC were evaluated, and their demographic and clinicopathologic variables were recorded. The log-rank test assessed the association of variables with overall survival (OS), recurrence-free survival (RFS), local recurrence, and distant metastasis.

Results

The median overall survival period was 51 months. The recurrence rate was 13.7%: 7.2% local and 9.3% distant recurrence. One-, two-, three-, and five-year RFS were 92%, 89%, 87%, and 83%, respectively, and OS were 96%, 93%, 90%, and 89%, respectively. Local and distant recurrence rates were higher in patients with higher tumor grades. Additionally, RFS and OS were worse in patients with higher tumor grades, and perforation was associated with worse OS.

Conclusions

The determinants of survival and recurrence identified in the present study can be used to improve patient outcomes by early diagnosis and appropriate management of high-risk patients.

Design and Development of a Non-invasive Opto-Electronic Sensor for Blood Glucose Monitoring Using a Visible Light Source

Background The management of diabetes is critically dependent on the continuous monitoring of blood glucose levels. Contemporary approaches primarily utilize invasive methods, which often prove to be uncomfortable and can deter patient adherence. There is a pressing need for the development of novel strategies that improve patient compliance and simplify the process of glucose monitoring. Aim and objectives The primary objective of this research is to develop a non-invasive blood glucose monitoring system (NIBGMS) that offers a convenient alternative to conventional invasive methods. This study aims to demonstrate the feasibility and accuracy of using visible laser light at a wavelength of 650 nm for glucose monitoring and to address physiological and technical challenges associated with in vivo measurements. Methods Our approach involved the design of a device that exploits the quantitative relationship between glucose concentration and the refraction phenomena of laser light. The system was initially calibrated and tested using glucose solutions across a range of concentrations (25-500 mg/dL). To get around the problems that come up when people's skin and bodies are different, we combined an infrared (IR) transmitter (800 nm) and receiver that checks for changes in voltage, which are indicative of glucose levels. Results The prototype device was compared with a commercially available blood glucose monitor (Accu-Chek active machine; Roche Diabetes Care, Inc., Mumbai, India). The results demonstrated an average linearity of 95.7% relative to the Accu-Chek machine, indicating a high level of accuracy in the non-invasive measurement of glucose levels. Conclusions The findings suggest that our NIBGMS holds significant promise for clinical application. It reduces the discomfort associated with blood sampling and provides reliable measurements that are comparable to those of existing invasive methods. The successful development of this device paves the way for further commercial translation and could significantly improve the quality of life for individuals with diabetes, by facilitating easier and more frequent monitoring.

Knowledge, Attitude, Awareness, and Future Expectations of Robotic Surgery in Patients Attending Surgical Specialties Clinics

Introduction The use of robotic-assisted surgeries (RAS) has been growing in surgical specialties. It allows surgeons to perform higher-quality operations with fewer complications, mortality, and morbidity. However, there are a lot of misconceptions about RAS among patients. Therefore, our study aimed to assess the knowledge, attitude, awareness, and future expectations of RAS in patients attending surgical clinics. Methods  A cross-sectional study was conducted in King Abdulaziz Medical City (KAMC) surgical clinics in Riyadh, Saudi Arabia. All participants <18 years of age were excluded. The questionnaire was distributed to 304 patients attending surgical clinics with a confidence level of 95% and a margin of error of 5%. Cluster sampling was used since the respondents were from multiple surgical specialties. Finally, multivariate analysis was performed to assess participants' preference for robotic surgery. Results Most participants (58.6%, n=178) were between 21 and 40 years old, and males were 52% of the participants. Many respondents thought a robot did not do the surgery. 70.7% of respondents had not heard of robotic surgery, with the media being the most common source of information. Internal damage was the prevalent concern (51.0%, n= 155) in malfunctions of robotic surgery. A significant relationship was found between participants from 21 to 40 years of age and a stronger preference for robotic surgery (p=.027). Respondents who preferred robotic surgery were discovered to have a significant relationship with participants who thought robotic surgery was safer and had better results (p<.001). 13.9% of participants who did not prefer robotic surgery also took cost into account significantly (χ2=28.93, p<.001, Cramer's V=.22). 67.2% (n=43) of respondents who preferred robotic surgery believed it might eventually replace present practices. Conclusion Our study concluded that the majority did not favor or were unsure whether to undergo robotic surgeries or not. However, most participants had some misconceptions and a lack of awareness about robotic surgeries. Raising awareness among patients can improve the mutual decision-making between them and their treating physician.

Clinical Application of an Artificial Intelligence System for Diagnosing Thyroid Disease Based on a Computer Neural Network Deep Learning Model

Purpose

This study aimed to establish a stereoscopic neural learning network through deep learning and construct an artificial intelligence (AI) diagnosis system for the prediction of benign and malignant thyroid diseases, as well as repeatedly verified the diagnosis system and adjusted the data, in order to develop a type of AI-assisted thyroid diagnosis software with a low false negative rate and high sensitivity for clinical practice.

Patients and Methods

From July 2020 to April 2023, A total of 36 patients with thyroid nodules in our hospital were selected for diagnosis of thyroid nodules based on the Expert Consensus on Thyroid Ultrasound; samples were taken by aspiration biopsy or surgically and sent for pathological diagnosis. The ultrasonic diagnosis results were compared with the pathological results, a database was established based on the ultrasonic diagnostic characteristics and was entered in the AI-assisted diagnosis software for judgment of benign and malignant conditions. The data in the software were corrected based on the conformity rate and the reasons for misjudgment, and the corrected software was used to evaluate the benign and malignant conditions of the 36 patients, until the conformity rate exceeded 90%.

Results

The initial conformity rate of the AI software for identifying benign and malignant conditions was 88%, while that of the software utilizing the database was 94%.

Conclusion

We established a stereoscopic neural learning network and construct an AI diagnosis system for the prediction of benign and malignant thyroid diseases, with a low false negative rate and high sensitivity for clinical practice.

Delays in Colorectal Cancer Screening for Latino Patients: The Role of Immigrant Healthcare in Stemming the Rising Global Incidence of Colorectal Cancer

The significant global burden of colorectal cancer accentuates disparities in access to preventive healthcare in most low- and middle-income countries (LMICs) as well as large sections of underserved populations within high-income countries. The barriers to colorectal cancer screening in economically transitioning Latin America are multiple. At the same time, immigration from these countries to the USA continues to increase. This case highlights the delays in diagnosis experienced by a recent immigrant from a country with no established colorectal cancer screening program, to an immigrant population in the USA with similar poor screening coverage. We discuss common challenges faced by Latinos in their home countries and the USA, as well as strategies that could be implemented to improve screening coverage in US immigrant populations.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Cloud platform to improve efficiency and coverage of asynchronous multidisciplinary team meetings for patients with digestive tract cancer

Background

Multidisciplinary team (MDT) meetings are the gold standard of cancer treatment. However, the limited participation of multiple medical experts and the low frequency of MDT meetings reduce the efficiency and coverage rate of MDTs. Herein, we retrospectively report the results of an asynchronous MDT based on a cloud platform (cMDT) to improve the efficiency and coverage rate of MDT meetings for digestive tract cancer.

Methods

The participants and cMDT processes associated with digestive tract cancer were discussed using a cloud platform. Software programming and cMDT test runs were subsequently conducted to further improve the software and processing. cMDT for digestive tract cancer was officially launched in June 2019. The doctor response duration, cMDT time, MDT coverage rate, National Comprehensive Cancer Network guidelines compliance rate for patients with stage III rectal cancer, and uniformity rate of medical experts' opinions were collected.

Results

The final cMDT software and processes used were determined. Among the 7462 digestive tract cancer patients, 3143 (control group) were diagnosed between March 2016 and February 2019, and 4319 (cMDT group) were diagnosed between June 2019 and May 2022. The average number of doctors participating in each cMDT was 3.26 ± 0.88. The average doctor response time was 27.21 ± 20.40 hours, and the average duration of cMDT was 7.68 ± 1.47 min. The coverage rates were 47.85% (1504/3143) and 79.99% (3455/4319) in the control and cMDT groups, respectively. The National Comprehensive Cancer Network guidelines compliance rates for stage III rectal cancer patients were 68.42% and 90.55% in the control and cMDT groups, respectively. The uniformity rate of medical experts' opinions was 89.75% (3101/3455), and 8.97% (310/3455) of patients needed online discussion through WeChat; only 1.28% (44/3455) of patients needed face-to-face discussion with the cMDT group members.

Conclusion

A cMDT can increase the coverage rate of MDTs and the compliance rate with National Comprehensive Cancer Network guidelines for stage III rectal cancer. The uniformity rate of the medical experts' opinions was high in the cMDT group, and it reduced contact between medical experts during the COVID-19 pandemic.

Future of Artificial Intelligence in Surgery: A Narrative Review

Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.

Examining the Applicability of Surgical Coaching Rules for Resident Autonomy in Non-teaching Hospitals

INTRODUCTION

This retrospective study aims to analyze the impact of standardized rules for teaching in university hospitals on surgical resident autonomy and patient safety, as measured by patient outcomes, and also examines the learning curves for residents and their impact on patient outcomes in a non-teaching hospital.

METHODS

The data for the study was collected retrospectively from medical records of 2000 adult patients who went through surgical procedures from January 2020 to December 2022. Participants were categorized into two groups based on the supervision level provided by attending surgeons and residents. Appropriate statistical methods were used to analyze the data.

RESULTS

It was observed that operative times of cases handled by both attending and resident surgeons were less than those handled by residents alone. On the other hand, the former group had a significantly higher burden of comorbidities and higher rate of perioperative complications than the latter. These results have important implications for the training of medical residents and the overall delivery of healthcare services in university hospitals.

CONCLUSION

The findings will also help towards better understanding of the effectiveness of these rules and their potential for improving the quality of care provided by residents in these settings.

Predictive Value of the Interaction between CEA and Hemoglobin in Neoadjuvant CCRT Outcomes in Rectal Cancer Patients

The adoption of neoadjuvant concurrent chemoradiotherapy (CCRT) has reshaped the therapeutic landscape, but response prediction remains challenging. This study investigates the interaction between pre-CCRT carcinoembryonic antigen (CEA) and post-CCRT hemoglobin (Hb) levels in predicting the response of locally advanced rectal cancer (LARC) to CCRT. Retrospective data from 93 rectal cancer patients receiving neoadjuvant CCRT were analyzed. Univariate analyses assessed clinical factors associated with tumor regression grade (TRG) and T-stage outcomes. Machine learning identified predictive biomarkers. Interaction effects between CEA and Hb were explored through subgroup analyses. Post-CCRT Hb varied between pre-CCRT CEA groups. The interaction between pre-CCRT CEA and post-CCRT Hb influenced TRG. Males with normal pre-CCRT CEA and anemia showed better treatment responses. Females with elevated pre-CCRT CEA and post-CCRT anemia exhibited poorer responses. The interaction effect between them was significant, indicating that their relationship with TRG was not additive. Inflammatory biomarkers, WBC, neutrophil count, and post-CCRT platelet level correlated with CCRT response. Contrasting with previous findings, anemia was a predictor of better treatment response in males with normal pre-CCRT CEA. The interaction between pre-CCRT CEA and post-CCRT Hb levels predicts the response of LARC to CCRT. CEA, Hb, and sex should be considered when assessing treatment response. Inflammatory biomarkers contribute to response prediction. Understanding these complex relationships can enhance personalized treatment approaches in rectal cancer patients.

Application of Virtual Reality-Assisted Exergaming on the Rehabilitation of Children with Cerebral Palsy: A Systematic Review and Meta-Analysis

BACKGROUND

Rehabilitation programs for children with cerebral palsy (CP) aim to improve their motor and cognitive skills through repeated and progressively challenging exercises. However, these exercises can be tedious and demotivating, which can affect the effectiveness and feasibility of the programs. To overcome this problem, virtual reality VR-assisted exergaming has emerged as a novel modality of physiotherapy that combines fun and motivation with physical activity. VR exergaming allows children with CP to perform complex movements in a secure and immersive environment, where they can interact with virtual objects and scenarios. This enhances their active engagement and learning, as well as their self-confidence and enjoyment. We aim to provide a comprehensive overview of the current state of research on VR exergaming for CP rehabilitation. The specific objectives are: To identify and describe the existing studies that have investigated the effects of VR exergaming on motor function and participation outcomes in children with CP. In addition, we aim to identify and discuss the main gaps, challenges, and limitations in the current research on VR exergaming for CP rehabilitation. Finally, we aim to provide recommendations and suggestions for future research and practice in this field.

METHODS

In June 2023, we conducted a systematic search on Scopus, Web of Science, PubMed, Cochrane, and Embase for randomized trials and cohort studies that applied VR-assisted exergaming to rehabilitating patients with CP. The inclusion criteria encompassed the following: (1) Randomized controlled trials (RCTs) and cohort studies involving the rehabilitation of children with CP; (2) the application of VR-based exergaming on the rehabilitation; (3) in comparison with conventional rehabilitation/usual care. The quality of the selected RCTs was evaluated using Cochrane's tool for risk of bias assessment bias includes. Whereas the quality of cohort studies was assessed using the National Institutes of Health (NIH) tool.

RESULTS

The systematic search of databases retrieved a total of 2576 studies. After removing 863 duplicates, 1713 studies underwent title and abstract screening, and 68 studies were then selected as eligible for full-text screening. Finally, 45 studies were involved in this review (n = 1580), and 24 of those were included in the quantitative analysis. The majority of the included RCTs had a low risk of bias regarding study reporting, participants' attrition, and generating a random sequence. Nearly half of the RCTs ensured good blinding of outcomes assessors. However, almost all the RCTs were unclear regarding the blinding of the participants and the study personnel. The 2020 retrospective cohort study conducted at Samsung Changwon Hospital, investigating the effects of virtual reality-based rehabilitation on upper extremity function in children with cerebral palsy, demonstrated fair quality in its methodology and findings. VR-assisted exergaming was more effective than conventional physiotherapy in improving the Gross Motor Function Measurement (GMFM)-88 score (MD = 0.81; 95% CI [0.15, 1.47], p-value = 0.02) and the GMFM walking and standing dimensions (MD = 1.45; 95% CI [0.48, 2.24], p-value = 0.003 and MD = 3.15; 95% CI [0.87, 5.42], p-value = 0.007), respectively. The mobility and cognitive domains of the Pediatric Evaluation of Disability Inventory score (MD = 1.32; 95% CI [1.11, 1.52], p-value < 0.001) and (MD = 0.81; 95% CI [0.50, 1.13], p-value < 0.0001) were also improved. The Canadian Occupational Performance Measure performance domain (MD = 1.30; 95% CI [1.04, 1.56], p-value < 0.001), the WeeFunctional Independence Measure total score (MD = 6.67; 95% CI [6.36, 6.99], p-value < 0.0001), and the Melbourne Assessment of Unilateral Upper Limb Function-2 score (p-value < 0.001) improved as well. This new intervention is similarly beneficial as conventional therapy in improving other efficacy measures.

CONCLUSIONS

Our findings suggest that VR-assisted exergaming may have some advantages over conventional rehabilitation in improving CP children's functioning and performance in daily life activities, upper and lower limb mobility, and cognition. VR-assisted exergaming seems to be as effective as conventional physiotherapy in the other studied function measures. With its potential efficacy, better feasibility, no reported side effects, and entertaining experience, VR-assisted exergaming may be a viable complementary approach to conventional physiotherapy in rehabilitating children with CP.

Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer

Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.

Risk Factor Analysis and Intervention Study for Unspecific Functional Enteropathy in a Maritime Environment: A Cross-Sectional Analytical Survey

Background

The study aimed to investigate the risk factors and interventions for unspecific functional bowel disorders (U-FBDs) in military personnel under maritime environment.

Methods

This cross-sectional analytical survey used the Rome III questionnaire for surveying 1018 military personnel involved in overseas humanitarian medical services from June 2013 to January 2016. Individuals diagnosed with U-FBDs were included in the U-FBDs group, while those without FBDs or other diseases were considered the control group. The psychological and sleep conditions of military personnel with U-FBDs were assessed using the SCL-90 scale and the Pittsburgh Sleep Quality Index scale, respectively. Health education and treatment were provided to individuals diagnosed with U-FBDs, and the improvements were evaluated after three months.

Results

Among 923 qualified questionnaires, 243 subjects was included in U-FBDs group and 240 in the control group. Smoking, alcohol consumption, and multiple seafaring missions were identified as risk factors for U-FBDs in military personnel on ocean-going missions. The U-FBDs group had significantly worse sleep quality, sleep efficiency, daytime dysfunction score, and total PSQI score compared to the control group (P < 0.05). Additionally, 10 factor scores of SCL-90 and the total score in the U-FBDs group were significantly higher than those in the control group (P < 0.01). Patients with U-FBDs also reported the highest rate of somatic symptoms (P < 0.01).

Conclusion

The onset of U-FBDs among military personnel on long-haul maritime may be closely related to mental, psychological, and sleep factors. Health education and treatment may help improve the symptoms of U-FBDs.

Combination of robot-assisted laparoscopy and ureteroscopy for the management of complex ureteral strictures

BACKGROUND

To summarize the efficacy of combined robot-assisted laparoscopy and ureteroscopy in treating complex ureteral strictures.

METHODS

Eleven patients underwent combined robot-assisted laparoscopy and ureteroscopy for ureteral strictures between January 2020 and August 2022. Preoperative B-ultrasound, glomerular filtration rate measurement, and intravenous pyelography showed different degrees of hydronephrosis in the affected kidney and moderate to severe stenosis in the corresponding part of the ureter. During the operation, stricture segment resection and end-to-end anastomosis were performed using the da Vinci robot to find the stricture point under the guidance of a ureteroscopic light source in the lateral or supine lithotomy position.

RESULTS

All the patients underwent robot-assisted laparoscopy and ureteroscopy combined with end-to-end ureterostenosis. There were no conversions to open surgery or intraoperative complications. Significant ureteral stricture segments were found in all patients intraoperatively; however, stricture length was not significantly different from the imaging findings. Patients were followed up for 3-27 months. Two months postoperatively, the double-J stent was removed, a ureteroscopy was performed, the ureteral mucosa at the end-to-end anastomosis grew well, and the lumen was patent in all patients. Furthermore, imaging examination showed that hydronephrosis was significantly improved in all patients, with grade I hydronephrosis in three cases and grade 0 hydronephrosis in eight cases. No recurrence of ureteral stricture was observed in patients followed up for > 1 year.

CONCLUSION

Robot-assisted laparoscopy combined with ureteroscopy is an effective method for treating complex ureteral strictures and can achieve accurate localization of the structured segment.

Dielectric Characterization of Breast Biopsied Tissues as Pre-Pathological Aid in Early Cancer Detection: A Blinded Feasibility Study

Dielectric characterization has significant potential in several medical applications, providing valuable insights into the electromagnetic properties of biological tissues for disease diagnosis, treatment planning, and monitoring of therapeutic interventions. This work presents the use of a custom-designed electromagnetic characterization system, based on an open-ended coaxial probe, for discriminating between benign and malignant breast tissues in a clinical setting. The probe's development involved a well-balanced compromise between physical feasibility and its combined use with a reconstruction algorithm known as the virtual transmission line model (VTLM). Immediately following the biopsy procedure, the dielectric properties of the breast tissues were reconstructed, enabling tissue discrimination based on a rule-of-thumb using the obtained dielectric parameters. A comparative analysis was then performed by analyzing the outcomes of the dielectric investigation with respect to conventional histological results. The experimental procedure took place at Complejo Hospitalario Universitario de Toledo-Hospital Virgen de la Salud, Spain, where excised breast tissues were collected and subsequently analyzed using the dielectric characterization system. A comprehensive statistical evaluation of the probe's performance was carried out, obtaining a sensitivity, specificity, and accuracy of 81.6%, 61.5%, and 73.4%, respectively, compared to conventional histological assessment, considered as the gold standard in this investigation.

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein-protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.

Barriers and facilitators to utilizing digital health technologies by healthcare professionals

Digital technologies change the healthcare environment, with several studies suggesting barriers and facilitators to using digital interventions by healthcare professionals (HPs). We consolidated the evidence from existing systematic reviews mentioning barriers and facilitators for the use of digital health technologies by HP. Electronic searches were performed in five databases (Cochrane Database of Systematic Reviews, Embase®, Epistemonikos, MEDLINE®, and Scopus) from inception to March 2023. We included reviews that reported barriers or facilitators factors to use technology solutions among HP. We performed data abstraction, methodological assessment, and certainty of the evidence appraisal by at least two authors. Overall, we included 108 reviews involving physicians, pharmacists, and nurses were included. High-quality evidence suggested that infrastructure and technical barriers (Relative Frequency Occurrence [RFO] 6.4% [95% CI 2.9-14.1]), psychological and personal issues (RFO 5.3% [95% CI 2.2-12.7]), and concerns of increasing working hours or workload (RFO 3.9% [95% CI 1.5-10.1]) were common concerns reported by HPs. Likewise, high-quality evidence supports that training/educational programs, multisector incentives, and the perception of technology effectiveness facilitate the adoption of digital technologies by HPs (RFO 3.8% [95% CI 1.8-7.9]). Our findings showed that infrastructure and technical issues, psychological barriers, and workload-related concerns are relevant barriers to comprehensively and holistically adopting digital health technologies by HPs. Conversely, deploying training, evaluating HP's perception of usefulness and willingness to use, and multi-stakeholders incentives are vital enablers to enhance the HP adoption of digital interventions.

The Use of 3D Printing Technology in Gynaecological Brachytherapy-A Narrative Review

Radiation therapy, including image-guided adaptive brachytherapy based on magnetic resonance imaging, is the standard of care in locally advanced cervical and vaginal cancer and part of the treatment in other primary and recurrent gynaecological tumours. Tumour control probability increases with dose and brachytherapy is the optimal technique to increase the dose to the target volume while maintaining dose constraints to organs at risk. The use of interstitial needles is now one of the quality indicators for cervical cancer brachytherapy and needles should optimally be used in ≥60% of patients. Commercially available applicators sometimes cannot be used because of anatomical barriers or do not allow adequate target volume coverage due to tumour size or topography. Over the last five to ten years, 3D printing has been increasingly used for manufacturing of customised applicators in brachytherapy, with gynaecological tumours being the most common indication. We present the rationale, techniques and current clinical evidence for the use of 3D-printed applicators in gynaecological brachytherapy.

Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?

Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.

The role of deep learning in diagnosing colorectal cancer

Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.

Establishment and Internal Validation of a Prognostic Score for Post-hepatectomy Liver Failure Based on Functional Liver Parameters Estimated via TC-99m GSA

Background The 99mTc-galactosyl human serum albumin (Tc-99m GSA) scintigraphy evaluates the future remnant liver function, which is an important prognostic factor for post-hepatectomy liver failure (PHLF). This study aimed to establish a new prognostic score for PHLF, including the functional liver parameters evaluated by Tc-99m GSA scintigraphy. Materials and methods This study reviewed a single-center, retrospective 368-patient database of those who underwent open and laparoscopic hepatectomy in Meiwa Hospital from January 2016 to October 2021. Moreover, 102 patients who underwent Tc-99m GSA scintigraphy following hepatectomy were analyzed. The index of blood clearance of the tracer was calculated from the uptake ratio of heart at 15 minutes to that at 3 minutes (HH15) and the index of hepatic accumulation was calculated from the uptake ratio of liver to liver plus heart at 15 minutes after the injection (LHL15) were calculated for the general functional parameters. The maximal removal rate of Tc-99m GSA (GSARmax) was also calculated, then the GSARmax of the remnant liver (GSARmax-RL) was estimated as the future remnant liver function depending on the hepatectomy. Multivariate analysis was conducted to identify the PHLF predictor, and then a risk-scoring system was established with the 1,000-times bootstrapped validation. Results PHLF (grade ≥ B) was observed in 13 of 102 patients. Multivariate analysis revealed that PHLF was independently predicted by GSARmax-RL (<0.26 mg/min) and LHL15 (<0.89). The risk score was assigned to each item and then classified into four subgroups, with a predicted PHLF of 3.7%, 14.4%, 42.8%, and 76.8%. Receiver operating characteristic (ROC) curve analysis demonstrated good discrimination (adjusted area under the curve (AUC) after bootstrapped validation, 0.779). The ROC curve analysis compared with other prognostic scores showed that the new model had the highest AUC values for accuracy. Conclusions The new prognostic score based on Tc-99m GSA scintigraphy could recognize patients with a high risk of progressing to PHLF and be helpful in planning therapeutic strategies.

Bioinformatics Screen Reveals Gli-Mediated Hedgehog Signaling as an Associated Pathway to Poor Immune Infiltration of Dedifferentiated Liposarcoma

Liposarcomas are the most diagnosed soft tissue sarcoma, with most cases consisting of well-differentiated (WDLPS) or dedifferentiated (DDLPS) histological subtypes. While both tumor subtypes can have clinical recurrence due to incomplete resections, DDLPS often has worse prognosis due to a higher likelihood of metastasis compared to its well-differentiated counterpart. Unfortunately, targeted therapeutic interventions have lagged in sarcoma oncology, making the need for molecular targeted therapies a promising future area of research for this family of malignancies. In this work, previously published data were analyzed to identify differential pathways that may contribute to the dedifferentiation process in liposarcoma. Interestingly, Gli-mediated Hedgehog signaling appeared to be enriched in dedifferentiated adipose progenitor cells and DDLPS tumors, and coincidentally Gli1 is often co-amplified with MDM2 and CDK4, given its genomic proximity along chromosome 12q13-12q15. However, we find that Gli2, but not Gli1, is differentially expressed between WDLPS and DDLPS, with a noticeable co-expression signature between Gli2 and genes involved in ECM remodeling. Additionally, Gli2 co-expression had a noticeable transcriptional signature that could suggest Gli-mediated Hedgehog signaling as an associated pathway contributing to poor immune infiltration in these tumors.

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

'Earlier than Early' Detection of Breast Cancer in Israeli BRCA Mutation Carriers Applying AI-Based Analysis to Consecutive MRI Scans

Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25-30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate 'earlier than early' BC diagnosis in BRCA PV carriers.

Evaluation of the Versius Robotic System for Infant Surgery-A Study in Piglets of Less than 10 kg Body Weight

BACKGROUND

We were able to demonstrate the feasibility of a new robotic system (Versius, CMR Surgical, Cambridge, UK) for procedures in small inanimate cavities. The aim of this consecutive study was to test the Versius^® system for its feasibility, performance, and safety of robotic abdominal and thoracic surgery in piglets simulating infants with a body weight lower than 10 kg.

METHODS

A total of 24 procedures (from explorative laparoscopy to thoracoscopic esophageal repair) were performed in 4 piglets with a mean age of 12 days and a mean body weight of 6.4 (7-7.5) kg. Additional urological procedures were performed after euthanasia of the piglet. The Versius^® robotic system was used with 5 mm wristed instruments and a 10 mm 3D 0° or 30° camera. The setup consisted of the master console and three to four separate arms. The performance of the procedure, the size, position, and the distance between the ports, the external and internal collisions, and complications of the procedures were recorded and analyzed.

RESULTS

We were able to perform all surgical procedures as planned. We encountered neither surgical nor robot-associated complications in the live model. Whereas all abdominal procedures could be performed successfully under general anesthesia, one piglet was euthanized early before the thoracic interventions, likely due to pulmonary inflammatory response. Technical limitations were based on the size of the camera (10 mm) being too large and the minimal insertion depth of the instruments for calibration of the fulcrum point.

CONCLUSIONS

Robotic surgery on newborns and infants appears technically feasible with the Versius^® system. Software adjustments for fulcrum point calibration need to be implemented by the manufacturer as a result of our study. To further evaluate the Versius^® system, prospective trials are needed, comparing it to open and laparoscopic surgery as well as to other robotic systems.

Detection of Tumour-Targeted IRDye800CW Tracer with Commercially Available Laparoscopic Surgical Systems

(1) Introduction: Near-infrared fluorescence (NIRF) combined with tumour-targeted tracers, such as bevacizumab-800CW, could aid surgical decision-making. This study explored the use of IRDye800CW, conjugated to bevacizumab, with four commercially available NIRF laparoscopes optimised for indocyanine green (ICG). (2) Methods: A (lymph node) phantom was made from a calibration device for NIRF and tissue-mimicking material. Serial dilutions of bevacizumab-800CW were made and ICG functioned as a reference. System settings, working distance, and thickness of tissue-mimicking material were varied to assess visibility of the fluorescence signal and tissue penetration. Tests were performed with four laparoscopes: VISERA ELITE II, Olympus; IMAGE1 S™ 4U Rubina, KARL STORZ; ENDOCAM Logic 4K platform, Richard Wolf; da Vinci Xi, Intuitive Surgical. (3) Results: The lowest visible bevacizumab-800CW concentration ranged between 13-850 nM (8-512 times diluted stock solution) for all laparoscopes, but the tracer was not visible through 0.8 cm of tissue in all systems. In contrast, ICG was still visible at a concentration of 0.4 nM (16,384 times diluted) and through 1.6-2.4 cm of tissue. Visibility and tissue penetration generally improved with a reduced working distance and manually adjusted system settings. (4) Conclusion: Depending on the application, bevacizumab-800CW might be sufficiently visible with current laparoscopes, but optimisation would widen applicability of tumour-targeted IRDye800CW tracers.

Evaluating the Development Status of Fluorescence-Guided Surgery (FGS) in Pediatric Surgery Using the Idea, Development, Exploration, Assessment, and Long-Term Study (IDEAL) Framework

Fluorescence-guided surgery (FGS) is used in many pediatric subspecialties but there are currently no standard guidelines or outcome data. We aimed to assess the current status of FGS in pediatrics using the Idea, Development, Exploration, Assessment, and Long-term study (IDEAL) framework. Clinical papers on FGS in children published from January 2000 to December 2022 were systematically reviewed. The stage of research development was measured considering seven fields of application: biliary tree imaging, vascular perfusion for gastrointestinal procedures, lymphatic flow imaging, tumor resection, urogenital surgery, plastic surgery, and miscellaneous procedures. Fifty-nine articles were selected. For each field of application, the overall IDEAL stage was determined to be 2a for biliary tree imaging (10 publications, 102 cases), 1 for vascular perfusion for gastrointestinal procedures (8 publications, 28 cases), 1 for lymphatic flow imaging (12 publications, 33 cases), 2a for tumor resection (20 publications, 238 cases), 2a for urogenital surgery (9 publications, 197 cases), and 1-2a for plastic surgery (4 publications, 26 cases). One report did not belong to any categories. FGS in children is still in an early phase of adoption and development. We recommend using the IDEAL framework as a guide and suggest developing multicenter studies to define the standard guidelines, effectiveness, and outcomes.

Telemedicine in Urology: Where Have We Been and Where Are We Heading?

Context

Humanity is facing significant challenges, and in 2019, a new coronavirus caused an unprecedented global disease outbreak. The coronavirus disease 2019 (COVID-19) pandemic vastly impacted health care delivery, generating devastating economic, social, and public health disruption. Although previously underutilized, it was not until recently that telemedicine emerged and amassed tremendous popularity.

Objective

To examine and assess telemedicine's past, present, and future roles in urology.

Evidence acquisition

We queried relevant literature investigating the role of telemedicine in urology using the electronic PubMed database and mainly focused on English-language studies of any design.

Evidence synthesis

Growing attention has been paid to the widespread adoption of novel telehealth technologies for managing various diseases. Meanwhile, solid evidence supports the meaningful use of telemedicine for most urological diagnoses. Existing literature delineates telemedicine as a viable, safe, and convenient alternative to in-person clinical visits.

Conclusions

The present article overviews the evolution of telemedicine in urology, and discusses its application in outpatient and physician's office settings. In addition, it highlights the technical, legal, ethical, and financial aspects of telemedicine while providing valuable insights and practical considerations for the future of telehealth in urology.

Patient summary

Urologists must adopt telemedicine carefully in daily practice, always adhering to predefined regulatory frameworks.

Preferences for Colorectal Cancer Screening Modalities Among the General Population in Saudi Arabia

BACKGROUND

Colorectal cancer (CRC) could be a leading explanation for cancer-related death. Numerous studies have shown the benefit of early screening for colorectal cancer in reducing mortality. Screening for colorectal cancer is a rational and cost-effective strategy for reducing the incidence of colorectal cancer and related mortality. Despite endorsement by academic and healthcare organizations, patient awareness and compliance with screening are low, partly due to patient-related barriers to screening.

AIM

This study aimed to explore the preferred screening method for colorectal cancer in Saudi Arabia in general.

METHODS

This is a cross-sectional study conducted among the Saudi adult population from September 2021 through February 2022. A self-developed questionnaire was distributed among the population using an online platform. Data were tabulated in Google Forms, and all statistical analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY).

RESULTS

During this study, data from 10,781 participants were analyzed. Among them, the most preferred screening modality for colorectal cancer was the stool fecal immunochemical test (41.7%) and the most suitable (33.5%). The most commonly mentioned qualities that influenced choosing a particular screening test were "how the test was performed" (50.4%).

CONCLUSION

Because the stool fecal immunochemical test is the most preferred screening modality for colorectal cancer, this study could serve as a database to aid in the implementation of a colorectal cancer screening program that meets the preferences of the general population of Saudi Arabia.

Development and Initial Assessment of a Novel and Customized Bile Duct Simulator for Handsewn Anastomosis Training

Simulation-based medical education allows for the training and maintenance of healthcare skills in a safe and controlled environment. In this technical report, the development and initial evaluation of a bile duct anastomosis simulator are described. The simulator was developed using additive manufacturing techniques such as three-dimensional (3D) printing and silicone work. The final product was produced by maxSIMhealth, a research lab at Ontario Tech University (Oshawa, ON, Canada), and included four individual silicone bile ducts, based on the expert opinions from surgeons at the Centre Hospitalier de l'Université de Montréal (Montreal, QC, Canada), and a 3D-printed maxSIMclamp, which was described in a previous report. The evaluation was conducted by nine individuals consisting of surgeons, surgical residents, and medical students to assess the fidelity, functionality, and teaching quality of the simulator. The results from the evaluation indicate that the simulator needs to improve its fidelity by being softer, thinner, and beige. On the other hand, the results also indicate that this simulator is extremely durable and can be used as a training tool for surgical residents with some minor improvements.

Application of Internet-based multidisciplinary management in patients with genitourinary cancers in China: A brief introduction to a new model of healthcare service for cancer survivors during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic has triggered multiple global healthcare system crises. Apart from the pandemic itself, the travel restriction and social distance policy for the purpose of epidemic control has cast a shadow on the management of cancer survivors. Cancer survivors suffered a double blow from both the epidemic and cancer. To deal with the challenge, we explored a new Internet-based patient management model. This model has overcome the limitation of time and space and thus can help oncologists to provide remote multidisciplinary healthcare services for cancer survivors. These patients can get high-quality cancer management from multidisciplinary experts without too much transportation. This model has been applied in patients with genitourinary cancers and proved to be effective and efficient. Our study demonstrated that more patients benefited from this model during the pandemic of COVID-19, especially in those affected heavily by COVID-19. These results suggested that it can also give insight into the management of other cancer survivors in China. Given the long-term impact of the COVID-19 pandemic, we would like to introduce our new model of healthcare service and the application of Internet-based multidisciplinary management to our global peers and medical industries to help their cancer survivors who are delayed in treatment due to the COVID-19 pandemic.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

A Review on Techniques and Biomaterials Used in 3D Bioprinting

Three-dimensional (3D) bioprinting is a cutting-edge technology that has come to light recently and shows a promising potential whose progress will change the face of medicine. This article reviews the most commonly used techniques and biomaterials for 3D bioprinting. We will also look at the advantages and limitations of various techniques and biomaterials and get a comparative idea about them. In addition, we will also look at the recent applications of these techniques in different industries. This article aims to get a basic idea of the techniques and biomaterials used in 3D bioprinting, their advantages and limitations, and their recent applications in various fields.