Video-based analysis of the blink reflex in Parkinson's disease patients.

We developed a video-based tool to quantitatively assess the Glabellar Tap Reflex (GTR) in patients with idiopathic Parkinson's disease (iPD) as well as healthy age-matched participants. We also video-graphically assessed the effect of dopaminergic medication on the GTR in iPD patients, as well as the frequency and blinking duration of reflex and non-reflex blinks. The Glabellar Tap Reflex is a clinical sign seen in patients e.g. suffering from iPD. Reliable tools to quantify this sign are lacking. METHODS: We recorded the GTR in 11 iPD patients and 12 healthy controls (HC) with a consumer-grade camera at a framerate of at least 180 images/s. In these videos, reflex and non-reflex blinks were analyzed for blink count and blinking duration in an automated fashion. RESULTS: With our setup, the GTR can be extracted from high-framerate cameras using landmarks of the MediaPipe face algorithm. iPD patients did not habituate to the GTR; dopaminergic medication did not alter that response. iPD patients' non-reflex blinks were higher in frequency and higher in blinking duration (width at half prominence); dopaminergic medication decreased the median frequency (Before medication-HC: p < 0.001, After medication-HC: p = 0.0026) and decreased the median blinking duration (Before medication-HC: p = 0.8594, After medication-HC: p = 0.6943)-both in the direction of HC. CONCLUSION: We developed a quantitative, video-based tool to assess the GTR and other blinking-specific parameters in HC and iPD patients. Further studies could compare the video data to electromyogram (EMG) data for accuracy and comparability, as well as evaluate the specificity of the GTR in patients with other neurodegenerative disorders, in whom the GTR can also be present. SIGNIFICANCE: The video-based detection of the blinking parameters allows for unobtrusive measurement in patients, a safer and more comfortable option.

Comparison of design methods for a safety run-in phase of a phase II clinical trial.

BACKGROUND/AIMS: In pediatric oncology, a Phase II trial often utilizes a safety run-in phase followed by an efficacy phase that enrolls at the dose level selected based on the safety run-in. Different from a Phase I trial, a Phase II safety run-in often assesses a very small number of dose levels. In the context of a safety run-in that assesses two or three dose levels, this article aims to compare three design methods, including the algorithm-based designs 3 + 3 and Rolling 6, and the model-assisted designs such as the Bayesian optimal interval design. METHODS: Extensive simulations were conducted to evaluate and compare operating characteristics of the three design methods for a safety run-in with two or three dose levels, varying the starting dose level. RESULTS: The performance of algorithm-based and model-assisted designs can be influenced by selection of the starting dose level, with trials starting at a lower dose level having a higher probability of selecting a low dose or considering all doses as toxic. The impact is larger for 3 + 3 and Rolling 6 but to a lesser extent for Bayesian optimal interval design. For a safety run-in with two dose levels, using 3 + 3 or Rolling 6 and starting at the higher dose often lead to similar performance to Bayesian optimal interval design. For safety run-in with three dose levels, starting at the middle dose with 3 + 3, Rolling 6 or Bayesian optimal interval design is a good compromise between improving correct dose selection and imposing a toxic dose to less patients. CONCLUSIONS: Despite being sensitive to the starting dose level, the 3 + 3, Rolling 6 and Bayesian optimal interval designs overall demonstrate reasonable performance, which can be further improved with wise selection of the starting dose level. The Rolling 6 design remains the recommended design method especially if pharmacokinetics is important or required with this design having the feature of treating six patients per dose level. When designing a safety run-in, selection of a design method or selection of a starting dose should consider both the performance of the design approaches with different choices of a starting dose level and the magnitude of safety concerns with the dose levels under investigation.

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19.

The outbreak of the COVID-19 epidemic has had a significant impact in increasing the number of emergency calls, which causes significant problems to emergency medical services centers (EMS) in many countries around the world, such as Saudi Arabia, which attracts a huge number of pilgrims during pilgrimage seasons. Among these issues, we address real-time ambulance dispatching and relocation problems (real-time ADRP). This paper proposes an improved MOEA/D algorithm using Simulated Annealing (G-MOEA/D-SA) to handle the real-time ADRP issue. The simulated annealing (SA) seeks to obtain optimal routes for ambulances to cover all emergency COVID-19 calls through the implementation of convergence indicator based dominance relation (CDR). To prevent the loss of good solutions once they are found in the G-MOEA/D-SA algorithm, we employ an external archive population to store the non-dominated solutions using the epsilon dominance relationship. Several experiments are conducted on real data collected from Saudi Arabia during the Covid-19 pandemic to compare our algorithm with three relevant state-of-art algorithms including MOEA/D, MOEA/D-M2M and NSGA-II. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our G-MOEA/D-SA algorithm.

Algorithm-based individualization methodology of the starting gonadotropin dose in IVF/ICSI and the freeze-all strategy prevent OHSS equally in normal responders: a systematic review and network meta-analysis of the evidence.

PURPOSE: Ovarian hyperstimulation syndrome (OHSS) represents a rare but dangerous condition associated with controlled ovarian stimulation (COS) in IVF/ICSI. Over the last decades, many strategies have been introduced into clinical practice with the objective of preventing this potentially life-threatening condition. Among these, the freeze-all policy has gained great popularity, thanks to improvements in vitrification. Nevertheless, not all clinics have adequate skills in vitrification procedures and patients may be dissatisfied with a longer time to pregnancy. METHODS: This study is a systematic review and network meta-analysis of randomized controlled trials comparing different strategies of ovarian stimulation in IVF/ICSI cycles (freeze-all policy, algorithm-based individualization of the starting dose, experience-based individualization of the starting dose, standard dose) in terms of reduction of OHSS, in normal responders. RESULTS: The results indicate that only the algorithm-based individualization of the starting gonadotropin dose reduces OHSS similarly to the freeze-all strategy. CONCLUSION: Albeit in the era of the freeze-all policy, the personalization of the starting gonadotropin dose obtained by the use of algorithms should be pursued as a valid and safe option for IVF.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Comparison of Students' Attitudes about the Effectiveness of Algorithm-based Education with Lecture-based Education.

Background: Training is a complex process, especially when the students are being prepared for patient's management. Therefore, the development of effective teaching methods is critical for to improvement of learning and communication between the content and concepts. In algorithm-based education, more focus is placed on more involvement of students in the subject, thereby providing a better understanding of the concept. In this study, we compared students' attitudes about the effectiveness of algorithm-based education (education based on the patient's complaints and symptoms) with lecture-based education in the learning ability of the medical students presented in the clinical course of the orthopedic group. Methods: This research is a single-group quasi-experimental study; we assessed the students' attitudes on a five-point Likert scale questionnaire with confirmed validity and reliability. The scores of two teaching methods were assessed after the training course, which was presented using the algorithmic method for selective titles and lectures for the other titles. Data were analyzed on SPSS software using a paired t-test. Results: A total of 220 internship medical students, including 58.7% of girls with a mean age of 22.9 ± 1.19 years, participated in the study. The mean score of the questions was 3.92±0.54 and 2.17±0.58 in the algorithmic and the lecture training, respectively. After comparing the results with a paired t-test, there was a significant difference between students' attitudes toward the two teaching methods (p ˂ 0.001), so the students' attitude was more positive toward the algorithm-based method. Conclusion: For the education of medical students, algorithm-based training is more efficacious compared to traditional methods such as lecture-based training.

[Telemedical care and IT-based systems in rheumatology]. / Telemedizinische Betreuung und IT-gestützte Verfahren in der Rheumatologie.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic and also the ever-increasing demands on the healthcare system, have led to a focus on the further development of telemedical services in rheumatology. OBJECTIVE: What is the evidence for telemedical services in rheumatology? MATERIAL AND METHODS: Narrative review of existing literature on telemedicine in rheumatology. RESULTS: Electronic patient reported outcomes (ePROs) can be determined by patients from their home and sent electronically to the rheumatologist. In future, ePROs may help with the decision whether a patient needs to attend the clinic for a visit or the visit can be rescheduled due to remission and well-being. Telemedicine has already been used for well-controlled patients with rheumatic diseases with good results in terms of safety and disease activity compared to conventional face-to-face visits. Telemedicine represents an interesting tool for appointment prioritization and triaging, while automated algorithm-based applications are currently too imprecise for routine clinical use. The role of smartphone applications in the care of patients with rheumatic diseases is still unclear. DISCUSSION: Telemedicine represents an interesting option for certain patient populations with rheumatic diseases. Apart from research on the effectiveness and safety of telemedical interventions, decision makers need to set clear rules on how telemedicine should be used to provide the best possible care for the individual patient.

The Impact of Intraoperative Patient Blood Management on Quality Development in Cardiac Surgery.

OBJECTIVES: Patient blood management (PBM) is increasingly introduced into clinical practice. Minimizing effects on transfusion have been proven, but relevance for clinical outcome has been sparsely examined. In regard to this, the authors analyzed the impact of introducing intraoperative PBM to cardiac surgery. DESIGN: Retrospective case-control study. SETTING: Single center. PARTICIPANTS: A total of 3,170 patients who underwent either coronary artery bypass grafting, isolated aortic valve replacement, or a combined procedure at the authors' institution between January 1, 2007, and December 31, 2015. INTERVENTION: In 2013, an intraoperative PBM service was established offering therapy recommendations on the basis of real-time laboratory monitoring. Comparisons to conventional coagulation management were adjusted for optimization of general, surgical, and perioperative care standards by interrupted time-series analysis and risk-dependent confounding by propensity- score matching. MEASUREMENTS AND MAIN RESULTS: Primary study endpoints were in-hospital mortality and morbidity. Morbidity was defined as clinically relevant prolongation of hospital stay, which was related to accumulation of postoperative complications. Transfusion requirements, bleeding, and thromboembolic complications were not treated as primary endpoints, but were also explored. The recommendations on the basis of real-time laboratory monitoring were adopted by the operative team in 72% of patients. Intraoperative PBM was associated independently with a reduction of morbidity (8.3% v 6.3%, p = 0.034), whereas in-hospitalmortality (3.0% v 2.6%, p = 0.521) remained unaffected. The need for red blood cell transfusion decreased (71.1% v 65.0%, p < 0.001), as did bleeding complications requiring surgical re-exploration (3.5% v 1.8%, p = 0.004). At the same time, stroke increased by statistical trend (1.0% v 1.9%, p = 0.038; after correction for imbalanced type of surgical procedure p = 0.085). CONCLUSIONS: Real-time laboratory recommendations achieved a high acceptance rate early after initiation. Improvement of clinical outcome by intraoperative PBM adds to the optimized surgical care. However, the corridor between hemostatic optimization and thromboembolic risk may be narrow.

Changing trends in the endourological management of urolithiasis in anomalous kidneys.

OBJECTIVES: To present our single-centre experience of urolithiasis management in anomalous kidneys, highlighting the need for an algorithm-based approach in deciding the appropriate treatment method for each type. PATIENTS AND METHODS: We retrospectively reviewed the data of all the patients who presented to our renal unit with urolithiasis in an anomalous kidney between January 1990 and July 2014. The peri-operative characteristics of all the patients, along with management approach, were studied. RESULTS: Percutaneous nephrolithotomy (PCNL) was the most common surgical technique used, followed by flexible ureterorenoscopy (URS). Pain was the most common presentation in these patients. The overall stone clearance rate associated with PCNL and flexible URS was 85.8% in cases of anomalous kidneys in this series. The stone clearance rates in ectopic kidneys, horseshoe kidneys, malrotated kidneys and autosomal dominant polycystic kidneys associated with PCNL and flexible URS were similar: 86%, 88%, 76% and 83%, respectively, and 81.2%, 80%, 92% and 100%, respectively . Based on our results, we developed a stone management algorithm. CONCLUSION: The management of renal calculi in patients with renal anomalies may vary based on the stone size, location, density, pelvicalyceal system anatomy and drainage. An algorithm-based approach could help surgeons decide the appropriate treatment in this population.

Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid-liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition.

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd2+ and Cu2+ ions in aqueous solutions. Multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN-MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd2+ and 98.3% for Cu2+). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N = 3), the quantitation limit(S/N = 10), and the linear range for Cu2+ were 0.05, 0.15, and 0.15-1000 µg L-1, respectively, and for Cd2+ were 0.07, 0.21, and 0.21-750 µg L-1, respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3 mg L-1 of Cu2+ and Cd2+ ions under the optimal conditions obtained by ANN-MOEA/D ranged from 94.8 to 105%.

The impact of major extended donor criteria on graft failure and patient mortality after liver transplantation.

INTRODUCTION: Numerous extended donor criteria (EDC) have been identified in liver transplantation (LT), but different EDC have different impacts on graft and patient survival. This study aimed to identify major EDC (maEDC) that were best able to predict the outcome after LT and to examine the plausibility of an allocation algorithm based on these criteria. METHODS: All consecutive LTs between 12/2006 and 03/2014 were included (n = 611). We analyzed the following EDC: donor age > 65 years, body mass index > 30, malignancy and drug abuse history, intensive care unit stay/ventilation > 7 days, aminotransferases > 3 times normal, serum bilirubin > 3 mg/dL, serum Na+ > 165 mmol/L, positive hepatitis serology, biopsy-proven macrovesicular steatosis (BPS) > 40%, and cold ischemia time (CIT) > 14 h. We analyzed hazard risk ratios of graft failure for each EDC and evaluated primary non-function (PNF). In addition, we analyzed 30-day, 90-day, 1-year, and 3-year graft survival. We established low- and high-risk graft (maEDC 0 vs. ≥ 1) and recipient (labMELD < 20 vs. ≥ 20) groups and compared the post-LT outcomes between these groups. RESULTS: BPS > 40%, donor age > 65 years, and CIT > 14 h (all p < 0.05) were independent predictors of graft failure and patient mortality and increased PNF, 30-day, 90-day, 1-year, and 3-year graft failure rates. Three-year graft and patient survival decreased in recipients of ≥ 1 maEDC grafts (all p < 0.05) and LT of high-risk grafts into high-risk recipients yielded worse outcomes compared with other groups. CONCLUSION: Donor age > 65 years, BPS > 40%, and CIT > 14 h are major EDC that decrease short and 3-year graft survival, and 3-year patient survival. An allocation algorithm based on maEDC and labMELD is therefore plausible.

Developing Charcot-Marie-Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based Spiking Neural Network.

In the developing technology Charcot-Marie-Tooth (CMT) disease is one of the teeth diseases which are occurred due to the genetic reason. The CMT disease affects the muscle tissue which reduces the progressive growth of the muscle. So, the CMT disease needs to be recognized carefully for eliminating the risk factors in the early stage. At the time of this process, the system handles the difficulties while performing feature extraction and classification part. So, the teeth images are processed by applying the normalization method which eliminates the salt and pepper noise from data. From that, modified group delay function along with Cepstral coefficient features are extracted with effective manner. After that Bacterial Foraging Optimization Algorithm based features are selected. Then the selected features are examined by applying the Bacterial Foraging Optimization Algorithm based spiking neural network which successfully recognizes the CMT disease. At that point the productivity of the framework is assessed with the assistance of exploratory outcomes.

A Survey on Underwater Acoustic Sensor Network Routing Protocols.

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy.

The growth of high-composition GeSn films in the future will likely be guided by algorithms. In this study, we show how a logarithmic-based algorithm can be used to obtain high-quality GeSn compositions up to 16% on GaAs (001) substrates via molecular beam epitaxy. Herein, we use composition targeting and logarithmic Sn cell temperature control to achieve linearly graded pseudomorph Ge1-xSnx compositions up to 10% before partial relaxation of the structure and a continued gradient up to 16% GeSn. In this report, we use X-ray diffraction, simulation, secondary ion mass spectrometry, and atomic force microscopy to analyze and demonstrate some of the possible growths that can be produced with the enclosed algorithm. This methodology of growth is a major step forward in the field of GeSn development and the first ever demonstration of algorithmically driven, linearly graded GeSn films.

Target flow deviations on the cardiopulmonary bypass cause postoperative delirium in cardiothoracic surgery-a retrospective study evaluating temporal fluctuations of perfusion data.

OBJECTIVES: Postoperative delirium (POD) is common, costly and associated with long-term morbidity and increased mortality. We conducted a cohort study to assess the contribution of cardiopulmonary bypass (CPB) to the development of POD by means of algorithm-based data processing. METHODS: A database was compiled from 3 datasets of patients who underwent cardiac surgery between 2014 and 2019: intensive care unit discharge files, CPB protocols and medical quality management records. Following data extraction and structuring using novel algorithms, missing data were imputed. Ten independent imputations were analysed by multiple logistic regression with stepwise deletion of factors to arrive at a minimal adequate model. RESULTS: POD was diagnosed in 456/3163 patients (14.4%). In addition to known demographic risk factors and comorbidities like male sex, age, carotid disease, acute kidney failure and diabetes mellitus, cardiopulmonary parameters like total blood volume at the CPB [adjusted odds ratio (AOR) 1.001; confidence interval (CI) 1.1001-1.002] were independent predictors of POD. Higher values of the minimal blood flow were associated with a lower risk of POD (AOR 0.993; CI 0.988-0.997). Flow rates at least 30% above target did emerge in the minimal adequate model as a potential risk factor, but the confidence interval suggested a lack of statistical significance (AOR 1.819; 95% CI: 0.955-3.463). CONCLUSIONS: CPB data processing proved to be a useful tool for obtaining compact information to better identify the roles of individual operational states. Strict adherence to perfusion limits along with tighter control of blood flow and acid-base balance during CPB may help to further decrease the risk of POD.

Automated SEM/EDX imaging for the in-depth characterization of non-exhaust traffic emissions from the Munich subway system.

A SEM/EDX based automated measurement and classification algorithm was tested as a method for the in-depth analysis of micro-environments in the Munich subway using a custom build mobile measurements system. Sampling was conducted at platform stations, to investigate the personal exposure of commuters to subway particulate matter during platform stays. EDX spectra and morphological features of all analyzed particles were automatically obtained and particles were automatically classified based on pre-defined chemical and morphological boundaries. Source apportionment for individual particles, such as abrasion processes at the wheel-brake interface, was partially possible based on the established particle classes. An average of 98.87 ± 1.06 % of over 200,000 analyzed particles were automatically assigned to the pre-defined classes, with 84.68 ± 16.45 % of particles classified as highly ferruginous. Manual EDX analysis further revealed, that heavy metal rich particles were also present in the ultrafine size range well below 100 nm.

Algorithm-based modular psychotherapy vs. cognitive-behavioral therapy for patients with depression, psychiatric comorbidities and early trauma: a proof-of-concept randomized controlled trial.

Effect sizes of psychotherapies currently stagnate at a low-to-moderate level. Personalizing psychotherapy by algorithm-based modular procedures promises improved outcomes, greater flexibility, and a better fit between research and practice. However, evidence for the feasibility and efficacy of modular-based psychotherapy, using a personalized treatment algorithm, is lacking. This proof-of-concept randomized controlled trial was conducted in 70 adult outpatients with a primary DSM-5 diagnosis of major depressive disorder, a score higher than 18 on the 24-item Hamilton Rating Scale for Depression (HRSD-24), at least one comorbid psychiatric diagnosis according to the Structured Clinical Interview for DSM-5 (SCID-5), a history of at least "moderate to severe" childhood maltreatment on at least one domain of the Childhood Trauma Questionnaire (CTQ), and exceeding the cut-off value on at least one of three measures of early trauma-related transdiagnostic mechanisms: the Rejection Sensitivity Questionnaire (RSQ), the Interpersonal Reactivity Index (IRI), and the Difficulties in Emotion Regulation Scale-16 (DERS-16). Patients were randomized to 20 sessions of either standard cognitive-behavioral therapy alone (CBT) or CBT plus transdiagnostic modules according to a mechanism-based treatment algorithm (MoBa), over 16 weeks. We aimed to assess the feasibility of MoBa, and to compare MoBa vs. CBT with respect to participants' and therapists' overall satisfaction and ratings of therapeutic alliance (using the Working Alliance Inventory - Short Revised, WAI-SR), efficacy, impact on early trauma-related transdiagnostic mechanisms, and safety. The primary outcome for efficacy was the HRSD-24 score at post-treatment. Secondary outcomes included, among others, the rate of response (defined as a reduction of the HRSD-24 score by at least 50% from baseline and a score <16 at post-treatment), the rate of remission (defined as a HRSD-24 score ≤8 at post-treatment), and improvements in early trauma-related mechanisms of social threat response, hyperarousal, and social processes/empathy. We found no difficulties in the selection of the transdiagnostic modules in the individual patients, applying the above-mentioned cut-offs, and in the implementation of MoBa. Both participants and therapists reported higher overall satisfaction and had higher WAI-SR ratings with MoBa than CBT. Both approaches led to major reductions of depressive symptoms at post-treatment, with a non-significant superiority of MoBa over CBT. Patients randomized to MoBa were nearly three times as likely to experience remission at the end of therapy (29.4% vs. 11.4%; odds ratio, OR = 3.2, 95% CI: 0.9-11.6). Among mechanism-based outcomes, MoBa patients showed a significantly higher post-treatment effect on social processes/empathy (p<0.05) compared to CBT patients, who presented an exacerbation on this domain at post-treatment. Substantially less adverse events were reported for MoBa compared to CBT. These results suggest the feasibility and acceptability of an algorithm-based modular psychotherapy complementing CBT in depressed patients with psychiatric comorbidities and early trauma. While initial evidence of efficacy was observed, potential clinical advantages and interindividual heterogeneity in treatment outcomes will have to be investigated in fully powered confirmation trials.

Coding and coverage for cardiac CT in the era of algorithm-based healthcare procedures and services.

In order for patients to gain the benefit of innovation in cardiac CT, it is necessary for coding, coverage, and payment to adapt to the novelty of algorithm-based healthcare procedures and services (ABHS). Appendix S to the CPT Code Set, the "AI Taxonomy", enables creation of discrete and differentiable codes for reimbursement of ABHS which has been clinically validated and FDA-labeled. Payment policy in OPPS and PFS is evolving to take account of the unique opportunities and issues arising from the clinical adoption of ABHS.

Real-time non-uniform surface refinement model for lung adenocarcinoma surgery.

Soft tissue models play a crucial role in virtual surgery. However, most existing methods use uniform meshes and overall refinement to construct inhomogeneous soft tissues for virtual lungs. This leads to a complex computation and poor model realism. Therefore, a real-time non-uniform surface refinement model (RNSM) for lung adenocarcinoma surgery is proposed in this paper. First, to better describe the inhomogeneous soft tissues, the tetrahedra are subdivided to different degrees depending on their densities, which reduce the model's complexity while ensuring accuracy. Second, to improve the model accuracy, the model surface is subdivided using the Loop subdivision method. Finally, an optimal algorithm based on deformation radius is designed to enhance the deformation in real-time, in which a linear attenuation method of physical quantities is used to simulate the deformation of the weak deformation regions directly, and the finite element method (FEM) is used for the strong deformation regions. The experimental results show that the model is more accurate and faster than the existing soft tissue models for lung adenocarcinoma surgery simulation.

A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.