Sensor-Based Discovery of Search and Palpation Modes in the Clinical Breast Examination.

PURPOSE: Successful implementation of precision education systems requires widespread adoption and seamless integration of new technologies with unique data streams that facilitate real-time performance feedback. This paper explores the use of sensor technology to quantify hands-on clinical skills. The goal is to shorten the learning curve through objective and actionable feedback. METHOD: A sensor-enabled clinical breast examination (CBE) simulator was used to capture force and video data from practicing clinicians (N = 152). Force-by-time markers from the sensor data and a machine learning algorithm were used to parse physicians' CBE performance into periods of search and palpation and then these were used to investigate distinguishing characteristics of successful versus unsuccessful attempts to identify masses in CBEs. RESULTS: Mastery performance from successful physicians showed stable levels of speed and force across the entire CBE and a 15% increase in force when in palpation mode compared with search mode. Unsuccessful physicians failed to search with sufficient force to detect deep masses ( F [5,146] = 4.24, P = .001). While similar proportions of male and female physicians reached the highest performance level, males used more force as noted by higher palpation to search force ratios ( t [63] = 2.52, P = .014). CONCLUSIONS: Sensor technology can serve as a useful pathway to assess hands-on clinical skills and provide data-driven feedback. When using a sensor-enabled simulator, the authors found specific haptic approaches that were associated with successful CBE outcomes. Given this study's findings, continued exploration of sensor technology in support of precision education for hands-on clinical skills is warranted.

Precision Education: The Future of Lifelong Learning in Medicine.

ABSTRACT: The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE).PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE "cycles" start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics , insights are generated to drive precision interventions . At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational levels, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture.The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.

Haptics: The Science of Touch As a Foundational Pathway to Precision Education and Assessment.

ABSTRACT: Clinical touch is the cornerstone of the doctor-patient relationship and can impact patient experience and outcomes. In the current era, driven by an ever-increasing infusion of point-of-care technologies, physical exam skills have become undervalued. Moreover, touch and hands-on skills have been difficult to teach due to inaccurate assessments and difficulty with learning transfer through observation. In this article, the authors argue that haptics, the science of touch, provides a unique opportunity to explore new pathways to facilitate touch training. Furthermore, haptics can dramatically increase the density of touch-based assessments without increasing human rater burden-essential for realizing precision assessment. The science of haptics is reviewed, including the benefits of using haptics-informed language for objective structured clinical examinations. The authors describe how haptic devices and haptic language have and can be used to facilitate learning, communication, documentation and a much-needed reinvigoration of physical examination, and touch excellence at the point of care. The synergy of haptic devices, artificial intelligence, and virtual reality environments are discussed. The authors conclude with challenges of scaling haptic technology in medical education, such as cost and translational needs, and opportunities to achieve wider adoption of this transformative approach to precision education.

Surgical Education.

This paper summarizes the proceedings of the joint European Surgical Association ESA/American Surgical Association symposium on Surgical Education that took place in Bordeaux, France, as part of the celebrations for 30 years of ESA scientific meetings. Three presentations on the use of quantitative metrics to understand technical decisions, coaching during training and beyond, and entrustable professional activities were presented by American Surgical Association members and discussed by ESA members in a symposium attended by members of both associations.

The Quantified Surgeon: A Glimpse Into the Future of Surgical Metrics and Outcomes.

This paper summarizes key points of the 2023 Southeastern Surgical Congress Laws Lecture. The focus of the presentation was on the use of advanced engineering technology to quantify surgical mastery. New concepts relating to the visual-haptic loop, mastery and perception, and mastery and technical decisions were introduced and shown in an empirical fashion to have relevance in procedural outcomes in a simulated setting. The major takeaway point is that surgical mastery can be quantified using advanced engineering technology, and this process will help to shorten the learning curve.

Do Individual Surgeon Preferences Affect Procedural Outcomes?

OBJECTIVES: Surgeon preferences such as instrument and suture selection and idiosyncratic approaches to individual procedure steps have been largely viewed as minor differences in the surgical workflow. We hypothesized that idiosyncratic approaches could be quantified and shown to have measurable effects on procedural outcomes. METHODS: At the American College of Surgeons (ACS) Clinical Congress, experienced surgeons volunteered to wear motion tracking sensors and be videotaped while evaluating a loop of porcine intestines to identify and repair 2 preconfigured, standardized enterotomies. Video annotation was used to identify individual surgeon preferences and motion data was used to quantify surgical actions. χ 2 analysis was used to determine whether surgical preferences were associated with procedure outcomes (bowel leak). RESULTS: Surgeons' (N=255) preferences were categorized into 4 technical decisions. Three out of the 4 technical decisions (repaired injuries together, double-layer closure, corner-stitches vs no corner-stitches) played a significant role in outcomes, P <0.05. Running versus interrupted did not affect outcomes. Motion analysis revealed significant differences in average operative times (leak: 6.67 min vs no leak: 8.88 min, P =0.0004) and work effort (leak-path length=36.86 cm vs no leak-path length=49.99 cm, P =0.001). Surgeons who took the riskiest path but did not leak had better bimanual dexterity (leak=0.21/1.0 vs no leak=0.33/1.0, P =0.047) and placed more sutures during the repair (leak=4.69 sutures vs no leak=6.09 sutures, P =0.03). CONCLUSIONS: Our results show that individual preferences affect technical decisions and play a significant role in procedural outcomes. Future analysis in more complex procedures may make major contributions to our understanding of contributors to procedure outcomes.

Open surgery tool classification and hand utilization using a multi-camera system.

PURPOSE: The goal of this work is to use multi-camera video to classify open surgery tools as well as identify which tool is held in each hand. Multi-camera systems help prevent occlusions in open surgery video data. Furthermore, combining multiple views such as a top-view camera covering the full operative field and a close-up camera focusing on hand motion and anatomy may provide a more comprehensive view of the surgical workflow. However, multi-camera data fusion poses a new challenge: A tool may be visible in one camera and not the other. Thus, we defined the global ground truth as the tools being used regardless their visibility. Therefore, tools that are out of the image should be remembered for extensive periods of time while the system responds quickly to changes visible in the video. METHODS: Participants (n = 48) performed a simulated open bowel repair. A top-view and a close-up cameras were used. YOLOv5 was used for tool and hand detection. A high-frequency LSTM with a 1-second window at 30 frames per second (fps) and a low-frequency LSTM with a 40-second window at 3 fps were used for spatial, temporal, and multi-camera integration. RESULTS: The accuracy and F1 of the six systems were: top-view (0.88/0.88), close-up (0.81,0.83), both cameras (0.9/0.9), high-fps LSTM (0.92/0.93), low-fps LSTM (0.9/0.91), and our final architecture the multi-camera classifier(0.93/0.94). CONCLUSION: Since each camera in a multi-camera system may have a partial view of the procedure, we defined a 'global ground truth.' Defining this at the data labeling phase emphasized this requirement at the learning phase, eliminating the need for any heuristic decisions. By combining a system with a high fps and a low fps from the multiple camera array, we improved the classification abilities of the global ground truth.

Using open surgery simulation kinematic data for tool and gesture recognition.

PURPOSE: The use of motion sensors is emerging as a means for measuring surgical performance. Motion sensors are typically used for calculating performance metrics and assessing skill. The aim of this study was to identify surgical gestures and tools used during an open surgery suturing simulation based on motion sensor data. METHODS: Twenty-five participants performed a suturing task on a variable tissue simulator. Electromagnetic motion sensors were used to measure their performance. The current study compares GRU and LSTM networks, which are known to perform well on other kinematic datasets, as well as MS-TCN++, which was developed for video data and was adapted in this work for motion sensors data. Finally, we extended all architectures for multi-tasking. RESULTS: In the gesture recognition task the MS-TCN++ has the highest performance with accuracy of [Formula: see text] and F1-Macro of [Formula: see text], edit distance of [Formula: see text] and F1@10 of [Formula: see text] In the tool usage recognition task for the right hand, MS-TCN++ performs the best in most metrics with an accuracy score of [Formula: see text], F1-Macro of [Formula: see text], F1@10 of [Formula: see text], and F1@25 of [Formula: see text]. The multi-task GRU performs best in all metrics in the left-hand case, with an accuracy of [Formula: see text], edit distance of [Formula: see text], F1-Macro of [Formula: see text], F1@10 of [Formula: see text], and F1@25 of [Formula: see text]. CONCLUSION: In this study, using motion sensor data, we automatically identified the surgical gestures and the tools used during an open surgery suturing simulation. Our methods may be used for computing more detailed performance metrics and assisting in automatic workflow analysis. MS-TCN++ performed better in gesture recognition as well as right-hand tool recognition, while the multi-task GRU provided better results in the left-hand case. It should be noted that our multi-task GRU network is significantly smaller and has achieved competitive results in the rest of the tasks as well.

Diversity, equity, and inclusion in presidential leadership of academic medical and surgical societies.

BACKGROUND: Our aim was to identify gender and racial disparities in presidential leadership for national medical and surgical organizations. METHODS: We located publicly sourced information on national medical organizations. Years between or since the first diverse presidents were analyzed using descriptive statistics and Mann Whitney U tests. RESULTS: Sixty-seven national medical and surgical organizations were surveyed. 70.8% (n = 34) diversified via gender first (White-female), whereas 26.1% (n = 14) had racial diversity first. Organizations with gender diversity first followed with an African American male president sooner than organizations who first diversified by race (14.7 ± 11.8 v. 27.6 ± 11.3 years, p = 0.018). No significant difference was observed for the third tier of diversification. CONCLUSIONS: Significant gender and racial leadership disparities in national medical organizations are still present. It is notable that organizations with female leaders had a shorter timeline to racial diversity. These findings help to inform strategies to promote and increase diversity, equity, and inclusion in national leadership.

Video-based fully automatic assessment of open surgery suturing skills.

PURPOSE: The goal of this study was to develop a new reliable open surgery suturing simulation system for training medical students in situations where resources are limited or in the domestic setup. Namely, we developed an algorithm for tools and hands localization as well as identifying the interactions between them based on simple webcam video data, calculating motion metrics for assessment of surgical skill. METHODS: Twenty-five participants performed multiple suturing tasks using our simulator. The YOLO network was modified to a multi-task network for the purpose of tool localization and tool-hand interaction detection. This was accomplished by splitting the YOLO detection heads so that they supported both tasks with minimal addition to computer run-time. Furthermore, based on the outcome of the system, motion metrics were calculated. These metrics included traditional metrics such as time and path length as well as new metrics assessing the technique participants use for holding the tools. RESULTS: The dual-task network performance was similar to that of two networks, while computational load was only slightly bigger than one network. In addition, the motion metrics showed significant differences between experts and novices. CONCLUSION: While video capture is an essential part of minimal invasive surgery, it is not an integral component of open surgery. Thus, new algorithms, focusing on the unique challenges open surgery videos present, are required. In this study, a dual-task network was developed to solve both a localization task and a hand-tool interaction task. The dual network may be easily expanded to a multi-task network, which may be useful for images with multiple layers and for evaluating the interaction between these different layers.

Developing a longitudinal database of surgical skills performance for practicing surgeons: A formal feasibility and acceptance inquiry.

BACKGROUND: We explored the feasibility and surgeons' perceptions of the utility of a longitudinal skills performance database. METHODS: A 10-station surgical skills assessment center was established at a national scientific meeting. Skills assessment volunteers (n = 189) completed a survey including opinions on practicing surgeons' skills evaluation, ethics, and interest in a longitudinal database. A subset (n = 23) participated in a survey-related interview. RESULTS: Nearly all participants reported interest in a longitudinal database and most believed there is an ethical obligation for such assessments to protect the public. Several interviewees specified a critical role for both formal and informal evaluation is to first create a safe and supportive environment. CONCLUSIONS: Participants support the construction of longitudinal skills databases that allow information sharing and establishment of professional standards. In a constructive environment, structured peer feedback was deemed acceptable to enhance and diversify surgeon skills. Large scale skills testing is feasible and scientific meetings may be the ideal location.

SAGES consensus recommendations on an annotation framework for surgical video.

BACKGROUND: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. METHODS: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. RESULTS: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. CONCLUSIONS: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.