1,721,150 research outputs found
Automated, objective and predictive evaluation of technical skills in robot-assisted surgery
No full textUse of 3D models for planning, simulation, and training in vascular surgery
No full textDear Editor,
We read with great interest the article by Pugliese et al. entitled “The clinical use of 3D printing in surgery” recently published by Updates in Surgery.
In the past years, 3D printing has seen an almost exponential growth in several fields, including medicine and surgery, as testified by the increasing number of published articles. This success was fostered by technological progresses on manufacturing processes allowing to build layer by layer 3D objects at higher resolution
Analytic description of the image to patient torso registration problem in image guided interventions
Full text linkObjective: The accurate registration of virtual pre-operative information of the human body anatomy, obtained as images
with imaging devices, with real intra-operative information is one of the key aspects on which effective Image Guided
Surgery (IGS) is based. The registration of pre-operative images on the real patient, during abdominal and thoracic
interventions, is influenced by many parameters, which in many cases are influenced each other, thus making it often
difficult to define the problem and consequently to solve it for each specific kind of intervention. The objective of this
paper is to obtain an analytic description of the 3D image to patient registration problem, which can be more intuitive than
the traditional textual descriptions.
Methods: The problem is formalized and various parameters affecting the registration are macro-classified in function of
their nature.
Results: The problem is analytically described discussing for each macro-category of parameters potential solutions to
avoid or to reduce their contribution to the registration error.
Conclusions: The availability of an analytic description of the image to patient torso registration problem can be
beneficial for teaching IGS, to describe existing registration strategies, and to search new ones for each kind of surgery
using a systematic approach
Should we use virtual simulators for surgical resident selection?
No full textTo the Editor:
we read with great interest the article by Gardner et al. entitled “How Much Are We Spending on Resident Selection?” recently published by Journal of Surgical Education [1].
Gardner et al. put the attention on the importance of a rigorous selection process to identify applicants who will be the best fit for training programs. In this analysis authors revealed the significant time and resources spent for the current resident selection process, with the average program spending approximately $100,000 annually. Moreover, examining the amount of time and efforts dedicated to applicant screening activities, they observed that residency coordinators were shouldering much of the burden, spending over 132 hours on the process.
Gardner et al., rightly, stressed the importance for program leaders to assess the efficacy and efficiency of their current selection procedures and identify additional methodologies to make the process more efficient. A key point of the high costs is the large number of interviews conducted. For these reason, Gardner et al. highlighted the importance of reducing the number of on-site interviews, administering customized assessments to eligible applicants early in the process to help the identification of candidate's potential, fit, and alignment with the program’s values and expectations.
Moreover, according to a survey, 30% of program directors from Fellowship Council in North America believe that graduates entering fellowships cannot independently and safely perform routine operations [2]. Therefore, it is important to adopt more efficient screening tools, such as online assessments, phone or video interviews, or assessment centers, to decrease the burden for both applicants and programs, but also to choose candidates with higher possibilities to become competent surgeons. In this context, the use of simulators could represent a valid option. The use of simulators for the training of surgical resident is increasingly widespread nowadays. However, in our opinion a possible alternative use of surgical simulators could be as tests for resident selection during their initial assessment. Indeed, there is an increasing interest for a reliable test as an objective assessment of the innate ability for psychomotor manipulative skills for surgery and as an integral component of the selection process for the many interns or house officers’ intent on a surgical career. Moreover, the restrictions on working hours in the USA and even more extremely in EU member states has increased the importance of such innate aptitudes for surgery because surgical resident should acquire technical skills quickly, or at least efficiently.
As pointed out by Gardner the selection procedure of surgical residents students is currently very complex because it includes many phases. But it is very important to ensure a high probability of selecting the most promising candidates in view of such high costs.. Unfortunately, at present the selection process does not consider manual dexterity among the determining factors, an increasingly important aspect after the advent of minimally invasive surgery that requires psychomotor skills (hand-eye coordination, lack or reduction of tactile feedback,...). Since virtual simulators are able to objectively evaluate psychomotor competences, an aptitude test based on a virtual simulator may complement the evaluation process. For example, two studies were done using virtual simulators for robotic surgery to evaluate the innate ability for surgery among medical students. [2,3]. Although the two studies differ in design, participants and used simulators, they have found very similar results. In fact the two studies showed almost the same distribution of the three groups with 6.6% and 5.8% exhibiting outstanding performance, and 11.6% and 11.0% with low level ability for manipulative skills compared to their peers. [3]. These data are in agreement with data reported by a study on medical students using a simulator for laparoscopic appendectomy that revealed a 15% of medical students with low aptitude to reach proficiency [4]. Furthermore, the value of simulators as an aptitude test on technical skills has been demonstrated in the Republic of Ireland by a study on candidates entering a higher surgical residency training program (equivalent to a Fellowship in the USA), which confirmed a high correlation between score at surgical simulators and overall assessment, based on education and academic records, progress in clinical surgical performance, research output, and interview assessment [5].
In conclusion, the use of virtual simulators for objective testing could be included to complement the selection process of residents. However, if in the future the simulators were added among the tools for the resident selection, the costs of the purchase of the simulators and of the dedicated personnel should be considered in the total costs. However, thanks to the increasing diffusion of laparoscopy and robotic surgery, the number of simulators for these surgical approaches is constantly growing and this could reduce purchasing costs. Moreover, their use could save on training costs during the residency because it would give the possibility to invest on the most promising candidates and not to invest on those with less potentia
Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality
No full textAccurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications
Patient-specific surgical simulator for the pre-operative planning of single-incision laparoscopic surgery with bimanual robots
No full textINTRODUCTION:
The trend of surgical robotics is to follow the evolution of laparoscopy, which is now moving towards single-incision laparoscopic surgery. The main drawback of this approach is the limited maneuverability of the surgical tools. Promising solutions to improve the surgeon's dexterity are based on bimanual robots. However, since both robot arms are completely inserted into the patient's body, issues related to possible unwanted collisions with structures adjacent to the target organ may arise.
MATERIALS AND METHODS:
This paper presents a simulator based on patient-specific data for the positioning and workspace evaluation of bimanual surgical robots in the pre-operative planning of single-incision laparoscopic surgery.
RESULTS:
The simulator, designed for the pre-operative planning of robotic laparoscopic interventions, was tested by five expert surgeons who evaluated its main functionalities and provided an overall rating for the system.
DISCUSSION:
The proposed system demonstrated good performance and usability, and was designed to integrate both present and future bimanual surgical robots
Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning
No full textGoal: Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. Methods: xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). Results: Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. Conclusions: this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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