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Background: The superiority of paclitaxel/ramucirumab over alternative therapeutic regimens in patients with gastric cancer has yet to be defined. Our aim was to evaluate whether second-line treatment with paclitaxel/ramucirumab is superior compared with other therapies. Patients and methods: Retrospective real-world data from patients with advanced adenocarcinoma of the stomach, gastroesophageal junction, or distal esophagus, treated at Departments of Medical Oncology, affiliated with the Hellenic Cooperative Oncology Group (HeCOG), were collected. All patients had received at least 2 months of second-line treatment. The primary endpoint was progression-free survival 1 (PFS1). Results: From March 2015 to March 2023, 179 patients received second-line treatment (median age 61.3 years). Of those, 77 (43%) received paclitaxel/ramucirumab, 21 (11.7%) irinotecan/5-fluorouracil (5-FU)/leucovorin, 16 (8.9%) docetaxel, and 65 (36.3%) other treatments. The efficacy of paclitaxel/ramucirumab was assessed by histological subtype: diffuse, intestinal, and mixed. For diffuse histology, the adjusted hazard ratio (aHR) for PFS1 was 1.03 [95% confidence interval (CI) 0.50-2.16] and for overall survival (OS) was 1.71 (95% CI 0.79-3.68). For intestinal histology, the aHR for PFS1 was 0.53 (95% CI 0.28-1.01) and for OS was 0.44 (95% CI 0.22-0.88), indicating a statistically significant OS benefit. Mixed histology showed no significant differences in PFS1 (aHR 1.00, 95% CI 0.23-4.37) or OS (aHR 1.10, 95% CI 0.32-3.82). Toxicity, dose reduction, and discontinuation rates were similar between paclitaxel/ramucirumab and other regimens. Conclusions: Second-line treatment with paclitaxel/ramucirumab was independently associated with OS compared with other regimens in patients with advanced intestinal-type gastric cancer. Identification of the most effective treatment for advanced gastric cancer remains a challenge.

Abstract The lateral patellofemoral ligament (LPFL) is an important anatomical structure and a stabilizer of the patella against medial subluxation or dislocation. It is located within the second layer of the lateral soft tissue of the knee, with firm attachments to the anterior‐distal region of the lateral femoral epicondyle and the the lateral edge of the patella and patellar tendon. LPFL has a non‐isometric character and decreases its length across the knee flexion, especially from 0° to 30°. Most LPFL injuries are iatrogenic (> 90%) and occur during lateral retinacular release, while the mainstay treatment option involves reconstruction of the torn ligament using an autograft or an allograft.

This review paper presents a systematic literature review on the use of Augmented Reality (AR) in engineering education, and specifically in student's spatial ability training, for the last decade. Researchers have explored the benefits of AR, and its application has been of increasing interest in all levels of education. Engineering students tend to have difficulties in acquiring visualization skills, and hence, AR is gaining momentum in enhancing students' learning achievements. This paper aims to present valuable information to researchers, tutors and software developers of learning technology systems concerning the advantages and limitations of AR in spatial ability training, the incorporation of adaptivity and personalization in AR applications as well as the aspects of spatial ability having been evaluated using AR and the prevalent evaluation methods for AR applications. To this direction, a total of thirty-two (32) studies were reviewed, having been published since 2010. The findings reveal an increase in the number of studies during the last three years. One major conclusion is the improvement of learners' spatial ability using AR in educational settings, and the noted challenge is the need for more learning content. One research gap that has been identified is the lack of personalization in the developed applications, offering space for future research. Concluding, this area is under-researched, and thus, there is scope for a lot of improvement.

Hands-on and virtual laboratory-based learning has been integrated into science education due to its potential positive impact on students’ knowledge and skills development. In this study, we explore the effect of the hands-on and virtual laboratories on 152 undergraduate students’ conceptual knowledge, inquiry, and measurement skills acquisition in the domain of operational amplifiers (op-amps) circuit learning. Students were divided into two groups and performed individually three experimental exercises involving basic op-amps electronic circuits: students in the Hands-On group performed the exercises in a physical laboratory environment, while students in the Virtual group performed the exercises in a virtual environment with TINA-TI (v9) software. Pre-post tests were used to quantify student performance progress stemming from their laboratory-type activities. Based on our findings, knowledge was developed the most, followed by inquiry skills, and finally, skills related to measuring electronic current quantities in a circuit, F(2,456) = 44.183, p = 0.000. Additionally, an ANCOVA analysis comparing the means of the three exercises revealed that the group participating in hands-on activities outperformed the group engaged in virtual activities, F(1,152) = 9.039, p = 0.003. Finally, we recommend designing a curriculum that focuses on both cognitive growth and skills development in the domain of op-amps.

Introduction: The direct reimplantation of autologous primary articular chondrocytes in a single-stage procedure for knee cartilage lesions is a novel approach, yet to be extensively reported. This study aimed to evaluate the clinical outcomes and reinterventions over a 5-year period post-surgery. Methods: A prospective case series involving 16 patients (4 female, 12 male) with single or multiple focal knee cartilage lesions was conducted. The mean age at baseline was 36.8 (±11.5) years. The mean total lesion size was 4.5 (±2.3) cm2 Patients underwent surgery where articular chondrocytes and mononuclear bone marrow cells were isolated, mixed, and seeded onto a hyaluronan-based scaffold within the lesion. A structured physical therapy regimen was followed, and patients were assessed using Knee injury and Osteoarthritis Outcome Score (KOOS) and International Knee Documentation Committee (IKDC) Subjective questionnaires at various intervals. Overall, patient-reported outcomes improved over the first 3 years post-surgery, with slight declines thereafter. Results: Significant improvements over baseline were noted for various KOOS parameters and IKDC Subjective scores at different follow-up points. Reinterventions were required for two patients, one receiving intra-articular injections with mesenchymal stromal cells and another undergoing knee washout for septic arthritis while a meniscus implant was removed. Patient satisfaction at final follow-up was generally favorable. Despite a significant intraoperative cell isolation time of approximately 1.5 hours, the procedure demonstrated safety and efficacy, with an average of ±0.9 million articular chondrocytes obtained per case. Conclusions: This study underscores the potential of coimplantation of intraoperatively isolated articular chondrocytes and bone marrow cells on a hyaluronan scaffold as a promising strategy for treating symptomatic knee cartilage lesions.

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Over the last decades, environmental awareness has provoked scientific interest in green energy, produced, among others, from solar sources. However, for the efficient operation and longevity of green solar plants, regular inspection and maintenance are required. This work aims to review vision-based monitoring techniques for the fault detection of photovoltaic (PV) plants, i.e., solar panels. Practical implications of such systems include timely fault identification based on data-driven insights and problem resolution, resulting in enhanced energy outputs, extended lifetime spans for PV panels, cost savings, as well as safe and scalable inspections. Details regarding the main components of PV systems, operation principles and key non-destructive fault detection technologies are included. Advancements in unmanned aerial vehicles (UAVs), as well as in artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods, offering enhanced monitoring opportunities, are in focus. A comparative analysis and an overall evaluation of state-of-the-art vision-based methods for detecting specific types of defects on PVs is conducted. The current performance and failures of vision-based algorithms for solar panel fault detection are identified, raising their capabilities, limitations and research gaps, towards effectively guiding future research. The results indicate that shading anomalies significantly impact the performance of PV units, while the top five fault detection methodologies, according to preset evaluation criteria, involve deep learning methods, such as CNNs and YOLO variations.

In modern industry, anomaly detection is an important part of safety and productivity management. Early anomaly detection could allow for timely interventions, preventing malfunctions and reducing risks for human workers and machines. This work aims to deliver an overview of the use of machine learning for anomaly detection in industrial environments, highlight the state-of-the-art, and discuss challenges and prospects for future research. Existing approaches, methodologies, and results related to anomaly detection are summarized, focusing on the application of machine learning for different types of industrial anomalies. Research findings indicate that, despite the current advances, there is still room for improvements and developments in machine learning-based anomaly detection in industrial environments, designating an important future field of research.

Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state of users for purposes that serve human–computer interaction. Studies of keystroke dynamics have used datasets created from volunteers recording fixed-text typing or free-text typing. Unfortunately, there are not enough keystroke dynamics datasets available on the Internet, especially from the free-text category, because they contain sensitive and personal information from the volunteers. In this work, a free-text dataset is presented, which consists of 533 logfiles, each of which contains data from 3500 keystrokes, coming from 164 volunteers. Specifically, the software developed to record user typing is described, the demographics of the volunteers who participated are given, the structure of the dataset is analyzed, and the experiments performed on the dataset justify its utility.

Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training dataset. A multi-step classification system is then proposed to classify the presence and type of porosity in a voxel, which can then be utilised to find the distribution of porosity within the build volume. Titanium parts were printed using a laser powder bed fusion system. Two discretisation techniques based on voxelisation were utilised: a defect-centric and a uniform discretisation method. Different machine learning models, feature sets, and other parameters were also tested. Promising results were achieved in identifying porous voxels; however, the accuracy of the classification requires improvement before being applied industrially. The potential of the voxelisation-based framework for this application and its ability to incorporate data from different stages of the additive manufacturing workflow as well as different machine learning models was clearly demonstrated.

The integration of the Arduino board into educational settings has penetrated across various educational levels. The teaching of this subject can be accomplished by (a) using real components in breadboards, (b) prefabricated modular boards that snap together, and (c) utilizing computer simulations. Yet, it is unknown which interface offers a more effective learning experience. Therefore, this experimental study aims to compare the effectiveness of these interfaces in a series of three laboratory exercises involving 110 university students, who were divided into three groups: (a) the first group used a tangible user interface, implementing circuits on breadboards, (b) the second group also used a tangible interface but with modular boards, and (c) the third group used a graphical user interface to simulate circuits using Tinkercad. For each laboratory exercise, students completed both pretests and posttests. Also, they provided feedback through five Likert-type attitude questions regarding their experiences. In terms of data analysis, t-tests, ANOVA, and ANCOVA, along with bootstrapping, and principal component analysis were employed. The results suggest that among the participants, those who used a graphical user interface stated that their understanding of the interconnection of components in microcontroller circuits was enhanced, while students with previous experience in microcontroller labs found the circuit creation process easier than students without experience.

Abstract Gene function conservation is crucial in molecular ecology, especially for key traits like growth and maturation in teleost fish. The vgll3 and six6 genes are known to influence age‐at‐maturity in Atlantic salmon, but their impact on other fish species is poorly understood. Here, we investigated the association of vgll3 and six6 in the domestication of gilthead seabream and European seabass, both undergoing selective breeding for growth‐related traits in the Mediterranean. We analysed two different sets of samples using two different genotyping approaches. The first dataset comprised farmed and wild populations from Greece, genotyped for SNPs within the two genes (‘gene‐level genotyping’). The second dataset examined 300–600 k SNPs located in the chromosomes of the two genes, derived from a meta‐analysis of a Pool‐Seq experiment involving farmed and wild populations distributed widely across the Mediterranean (‘chromosome‐level genotyping’). The gene‐level analysis revealed a statistically significant allele frequency differences between farmed and wild populations on both genes in each species. This finding was partially supported by the chromosome‐level analysis, identifying highly differentiated regions may be involved in the domestication process at varying distances from the candidate genes. Noteworthy genomic features were found, such as a CpG island in gilthead seabream and novel candidate genes in European seabass, warranting further investigation. These findings support a putative role of vgll3 and six6 in the maturation and growth of gilthead seabream and European seabass, emphasizing the need for further research on their conserved function.

The fast-paced advancement in multimedia production and exchanges over unsecured networks have led to a dire need to develop security applications. In this regard, chaos theory has much to offer, especially high-dimensional (HD) chaotic functions of fractional order. The authors propose a new symmetric, secure and robust image encryption method in this research work. In this method, the authors hybridize the Chen and Chua chaotic functions with a Memristor circuit to benefit from the strengths of each. Such a hybridization of systems allows for the generation of pseudo-random numbers which are used to develop encryption keys and substitution boxes (S-boxes). For the application of the generated encryption keys towards carrying out data diffusion, instead of the most commonly used approach of bit-stream level $XOR$ , this work utilizes different logical and arithmetic operations, which is made possible by performing this process over variable numerical bases. Moreover, multiple S-boxes of varying base- $n$ are generated and utilized in a parallel fashion, carrying out data confusion. The computed numerical results reflect the superior capabilities of the proposed image encryption technique, signifying resilience and robustness against various attacks.

Plant diseases are one of the primary causes of decreased agricultural production quality and quantity. With ongoing changes in plant structure and cultivation techniques, new diseases are constantly arising on plant leaves. Thus, accurate classification and detection of plant leaf diseases in their early stages will limit the spread of the infection and support the healthy development of plant production. This work proposes a novel lightweight deep convolutional neural network (CNN) model for obtaining high-level hidden feature representations. The deep features are then fused with traditional handcrafted local binary pattern (LBP) features to capture local texture information in plant leaf images. The proposed model is trained and tested on three publicly available datasets (Apple Leaf, Tomato Leaf, and Grape Leaf). On the three datasets, the proposed approach achieves 99%, 96.6%, and 98.5% validation accuracies, respectively, and 98.8%, 96.5%, and 98.3% test accuracies, respectively. The results of the experiments show that the proposed approach can provide a better control solution for plant diseases.

Knowing some characteristics of an unknown user is useful information for security and commercial purposes. One of the acquired characteristics is the user’s native language, and its recognition can be achieved with data derived from the text he/she types, since text is the most widespread means of communication between Internet users. Keystroke dynamics, which leverages data derived from how user types, ensures that no sensitive data are leaked. In this work, data from the daily typing of users of five different native languages are collected, keystroke dynamics features are extracted, the most suitable ones are selected using a feature selection algorithm, well-known machine learning models and a boosting algorithm are used, and a rate of correct prediction that exceeds 90% is achieved. Knowing a user’s native language can help strengthen authentication systems, make better use of online services, and protect unsuspecting users from falling victim to fraud.

Skin cancer is a senior public health issue that could profit from computer-aided diagnosis to decrease the encumbrance of this widespread disease. Researchers have been more motivated to develop computer-aided diagnosis systems because visual examination wastes time. The initial stage in skin lesion analysis is skin lesion segmentation, which might assist in the following categorization task. It is a difficult task because sometimes the whole lesion might be the same colors, and the borders of pigment regions can be foggy. Several studies have effectively handled skin lesion segmentation; nevertheless, developing new methodologies to improve efficiency is necessary. This work thoroughly analyzes the most advanced algorithms and methods for skin lesion segmentation. The review begins with traditional segmentation techniques, followed by a brief review of skin lesion segmentation using deep learning and optimization techniques. The main objective of this work is to highlight the strengths and weaknesses of a wide range of algorithms. Additionally, it examines various commonly used datasets for skin lesions and the metrics used to evaluate the performance of these techniques.

Globalization of markets involves new strategies and price policies from professionals that contribute to global competitiveness. Airline companies are changing tickets’ prices very often considering a variety of factors based on their proprietary rules and algorithms that are searching for the most suitable price policy. Recently, Artificial Intelligence (AI) models are exploited for the latter task, due to their compactness, fast adaptability, and many potentials in data generalization. This paper represents an analysis of airfare price prediction towards finding similarities in the pricing policies of different Airline companies by using AI Techniques. More specifically, a set of effective features is extracted from 136.917 data flights of Aegean, Turkish, Austrian and Lufthansa Airlines for six popular international destinations. The extracted set of features is then used to conduct a holistic analysis from the perspective of the end user who seeks the most affordable ticket cost, considering a destination-based evaluation including all airlines, and an airline-based evaluation including all destinations. For the latter cause, AI models from three different domains and a total of 16 model architectures are considered to resolve the airfare price prediction problem: Machine Learning (ML) with eight state-of-the-art models, Deep Learning (DL) with six CNN models and Quantum Machine Learning (QML) with two models. Experimental results reveal that at least three models from each domain, ML, DL, and QML, are able to achieve accuracies between 89% and 99% in this regression problem, for different international destinations and airline companies.

Background: In cases of complex arthroscopic knee surgery in the lateral compartment, such as performing lateral meniscus repair or transplantation, a tight lateral compartment can jeopardize the best possible care and could lead to iatrogenic cartilage injury. This technique shows a way to increase arthroscopic working space in a tight lateral compartment by performing an osteotomy of the femoral insertion of the lateral collateral ligament (LCL), utilizing a novel adjustable loop refixation technique. Indication: The femoral LCL insertion osteotomy can be performed if increased visualization and working space of the lateral compartment are needed during the complex arthroscopic knee surgery. Technique Description: After identification of the LCL femoral insertion, a 2-mm drill is passed through the LCL insertion to prepare for an anatomic reduction. The osteotomy is performed by taking a small bone plug together with the complete LCL insertion. Increased visualization and working space in the lateral compartment are obtained without damaging the intrinsic LCL structure. For reinsertion, the bone plug and proximal LCL is whipstitched with a high-strength suture and fixated to an adjustable loop Ultrabutton. The adjustable loop is shuttled through a predrilled 4.5-mm femoral tunnel and flipped on the medial side. The adjustable button is tensioned in 30° of flexion until the bone plug is anatomically reduced. Results: We present 1 patient who underwent a femoral LCL osteotomy during arthroscopic lateral meniscus allograft transplantation. The osteotomy healed without any issues, and there was no residual LCL laxity; which was confirmed with varus stress radiographs. Discussion/Conclusion: A femoral LCL insertion osteotomy can release a tight lateral compartment without damaging the intrinsic LCL structure. The adjustable loop fixation avoids the use of more traditional screw and washer fixation techniques, which tend to be more prominent and have the potential to back out. An osteotomy is more invasive than the “pie-crusting” technique of the medial collateral ligament for a tight medial compartment. However, it is required due to the poor intrinsic healing capacity of the LCL. Care should be taken to anatomically reduce the bone plug to avoid iatrogenic creation of LCL laxity. Patient Consent Disclosure Statement: The author(s) attests that consent has been obtained from any patient(s) appearing in this publication. If the individual may be identifiable, the author(s) has included a statement of release or other written form of approval from the patient(s) with this submission for publication.

The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity.

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors’ performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator’s poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.

We investigated the prevalence and genetic diversity of Wolbachia pipientis strains in Aedes albopictus populations in Greece. Using a combination of PCR and Sanger sequencing techniques, we genotyped Wolbachia strains in 105 mosquitoes collected across eight different administrative regions in 2021. We found a high prevalence of Wolbachia in both male (90%) and female (97%) mosquitoes. Among the infected samples, 84% had double infections with both wAlbA and wAlbB strains, while 16% had infections with only wAlbB. Our comparison of the Multi-Locus Sequence Typing (MLST) profile, employing gatB–coxA–hcpA–ftsZ–fbpA genotyping, revealed a single MLST profile for each wAlbA and wAlbB strain in Greek populations. The same MLST profiles were also reported in populations from China, Russia, and Argentina, suggesting low levels of global diversity in wAlbA and wAlbB strains. Furthermore, our results indicated a significant association between temperature and the prevalence of single infections (p = 6.498 × 10−7), with higher temperatures correlating with an increased likelihood of single infections. Although male bias showed a tendency towards single infections, the effect was marginally non-significant (p = 0.053). These results were confirmed using a bootstrap-with-replacement analysis approach. Overall, our findings offer novel insights into the distribution and species diversity of Wolbachia strains in Greek Ae. albopictus populations, emphasizing the importance of understanding the short-term plastic and adaptive responses of these organisms to environmental stressors and rapid climate change.

Introduction: Transoral robotic surgery (TORS) is an exciting, rapidly evolving and, since 2009, widely accepted (FDA approved) field that offers a new minimally invasive surgical practice for head and neck diseases. The aim of this study is to assess the feasibility and safety of TORS, using the Da Vinci Xi surgical system, both for therapeutic and diagnostic purposes in head and neck cancer at our institution. Material and methods: From 2018 until today, 11 patients with a minimum follow-up of two years underwent TORS at Hygeia Hospital by a single surgeon with the Da Vinci Xi surgical system. All patients had squamous cell carcinoma (SCCA) of the oropharynx or unknown primary. 6 TORS were therapeutic (radical tonsillectomy and base of tongue resections) and 5 were diagnostic procedures for unknown primaries. Results: All 6 patients who underwent therapeutic surgery had resections with microscopic negative margins. Half of the patients had HPV positive disease. All patients except one remain free of disease until now. Three patients received adjuvant radiotherapy. No patient received chemotherapy. In the diagnostic group the primary tumor was identified in two patients. There were no serious complications. There was only one minor postoperative oropharyngeal bleeding, which was treated successfully with non-operative means. Conclusions: TORS is a safe treatment alternative and it also allows identification of the unknown primary.

Automated solutions for medical diagnosis based on computer vision form an emerging field of science aiming to enhance diagnosis and early disease detection. The detection and quantification of facial asymmetries enable facial palsy evaluation. In this work, a detailed review of the quantification of facial palsy takes place, covering all methods ranging from traditional manual mathematical modeling to automated computer vision-based methods. Moreover, facial palsy quantification is defined in terms of facial asymmetry indices calculation for different image modalities. The aim is to introduce readers to the concept of mathematical modeling approaches for facial palsy detection and evaluation and present the process of the development of this separate application field over time. Facial landmark extraction, facial datasets, and palsy grading systems are included in this research. As a general conclusion, machine learning methods for the evaluation of facial palsy lead to limited performance due to the use of handcrafted features, combined with the scarcity of the available datasets. Deep learning methods allow the automatic learning of discriminative deep facial features, leading to comparatively higher performance accuracies. Datasets limitations, proposed solutions, and future research directions in the field are also presented.

Abstract Purpose This systematic review evaluated the clinical outcomes of hardware-free MPFL reconstruction techniques in patients with recurrent patellofemoral instability, focusing on patient-reported outcome measures (PROMs), redislocation rate, and complications. The hypothesis was that hardware-free MPFL reconstruction in patients with recurrent patellofemoral instability is safe and effective. Methods This systematic review was conducted following the PRISMA guidelines. PubMed, Scopus, and Virtual Health Library databases were accessed in October 2021. All the clinical studies investigating the efficacy and feasibility of hardware-free MPFL reconstruction were screened for inclusion. Only studies with a minimum 24-month follow-up were considered eligible. Kujala Anterior Knee Pain Scale improvement and redislocation rate after surgical treatment were evaluated as primary outcomes. The rate of postoperative complications was evaluated as a secondary outcome. The quality of the methodological assessment was assessed using the Modified Coleman Methodology Score. Results Eight studies were included in the present systematic review. The quality of the methodological assessment was moderate. Short- to long-term improvement of Kujala score was observed in all included studies. Mean score improvement ranged from + 13.2/100 to + 54/100, with mean postoperative scores ranging from 82/100 to 94/100. Patellar redislocation was observed in 8.33% (8 of 96) patients. Conclusion Hardware-free MPFL reconstruction with or without associated soft-tissue or bony realignment procedures provided reliable clinical improvements and was associated with a low rate of redislocation in patients with recurrent patellofemoral instability. Advantages such as safety, femoral physis preservation, and comparable complication profiles with implant-based techniques endorse its implementation. Orthopedic surgeons in cost-sensitive environments may also benefit their patients with lower costs, no need for implants, lack of implant-related complications, or surgery for implant removal. Level of evidence: Level IV.