Context.—

Smart glasses are a wearable technology that enable hands-free data acquisition and entry.

Objective.—

To develop a surgical pathology grossing application on a smart glass platform.

Design.—

An existing logistics software for the Google Glass Enterprise smart glass platform was used to create surgical pathology grossing protocols. The 2 grossing protocols were developed to simulate grossing a complex (heart) and a simple (kidney) specimen. For both protocols, users were visually prompted by the smart glass device to perform each task, record measurements, or document the field of view. In addition to measuring the total time of the protocol performance, each substep within the protocol was automatically recorded. Subsequently, a report was generated that contained the dictation, images, voice recordings, and the timing of each step. The application was tested by 3 users using the 2 grossing protocols. The users were tracked across 3 grossing procedures for each protocol.

Results.—

For the complex specimen grossing the average time across repeated procedures was not significantly different between users (P > .99). However, when grossing times of the complex specimen were compared for repeated performances of the same user, a significant reduction in grossing times was observed with each repetition (P = .002). For the simple specimen, the average grossing time across multiple attempts was different among users (P = .03); however, no improvement in grossing time was observed with repeated performance (P = .499).

Conclusions.—

Augmented reality based grossing applications can provide automated data collection to track the changes in grossing performance over time.

In the last decade there has been a rapid advancement in the use of information and communication technologies to empower the practice of medicine. Wearable smart glasses, such as Google Glass (Mountain View, California) or Microsoft HoloLens (Redmond, Washington), are examples of internet-connected devices that enable hands-free data entry and transmission of multiple types of data. Smart glasses deliver standard computer functions by a head mounted display. Smart glasses have been adopted by logistics and manufacturing companies with the aim of remote viewing, teleconferencing, documentation, and quality control in complex processes.

Pathologists deal with multiple specimen types, and each of these specimens must be handled and processed according to specific protocols. Although protocols for standardized procedures exist, a uniform assessment system to track and assess the quality of specimen grossing is a challenge. We used a commercial logistics software to build an interactive surgical pathology grossing manual within a smart glasses' platform. We used this platform to investigate the utility of smart glasses as a tool during grossing procedures.

This study did not involve human tissues, materials, or patient information.

A digital surgical pathology grossing application was developed in logistics software (Proceedix, Ghent, Belgium) for use on the Google Glass Enterprise wearable smart glasses platform (Figure 1A). Proceedix is a cloud-based logistics software not previously used in clinical laboratories. Proceedix has modular features to create interactive procedures for use on the Google Glass platform. Google Glass is an augmented reality device that connects to a computer or smartphone via wireless network or Bluetooth. Users operate Google Glass with voice commands and receive projected images from the device (Figure 1B). The smart glasses headset directs an interactive screen into the users' field of vision. The user is visually prompted by the smart glasses device to perform each task, record measurements, or document the field of view. All interactions with the application are performed in a hands-free manner. The completed grossing reports contain the user's input and are archived on a cloud-based server hosted by the application's manufacturer. The total time of the protocol performance and each substep within the protocol and location were automatically recorded under individual user accounts.

Figure 1

Smart Glasses Device. The smart glasses used in this study were Google Glass, which is a wearable device similar to eyeglasses (A). As shown in the photograph, there is an interactive module (Video display and Speaker) placed over the user's right eye and ear. A camera surveying the user's field of view is also present. The video display projects into the user's eye; an example of a video display screen shot is depicted (B). Photograph and screenshot captured by the authors of this manuscript.

Figure 1

Smart Glasses Device. The smart glasses used in this study were Google Glass, which is a wearable device similar to eyeglasses (A). As shown in the photograph, there is an interactive module (Video display and Speaker) placed over the user's right eye and ear. A camera surveying the user's field of view is also present. The video display projects into the user's eye; an example of a video display screen shot is depicted (B). Photograph and screenshot captured by the authors of this manuscript.

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Sheep organs (heart and kidney) were obtained from a food supplier (American Butchers, Dallas, Texas). Organs were received either fresh or frozen; frozen specimens were thawed on ice prior to dissection. The heart and a kidney dissecting protocols were selected as examples to represent complex and simple surgical specimens.

The organ-grossing protocols were programmed for heart (complex) and kidney (simple) explants. For heart dissection, the key protocol steps were weighing and determining overall organ dimensions, evaluating coronary blood vessels, dissecting cardiac chambers, measuring wall thickness, and assessing cardiac valves (Figure 2). For kidney dissection, the key protocol steps were weighing the organ, assessing overall dimensions, evaluating vessels at the hilum, bivalving, and describing the parenchymal surface and the ureters. For both protocols, the user was visually prompted by the application to perform each task, record a measurement, or document the field of view. When the protocol was finished, an automated report was generated that contained the dictation, images and video clips, voice recordings, and the timing of each step. The protocols for heart and kidney grossing were modified from publicly available examples (http://www.scvp.net/Dissection.html; https://www.uclahealth.org/pathology/genitourinary; both accessed on 4/14/2020).

Figure 2

Grossing algorithm for heart explants. A process diagram of the application created within the software Proceedix is shown. Each discrete process of grossing is represented by a separate blue box. Each process requires documentation of observations and/or measurements. Navigation of the application is performed by voice commands. In brief, the green circle indicates the start process, which is followed by preparation of cassettes, measuring the heart weight, and evaluation of the coronary arteries. A decision tree is shown with the red ovals which is guided by the absence or presence of coronary artery disease (CAD). The process continues until the last process, which is the listing of the cassettes and then exiting the program (orange circle).

Figure 2

Grossing algorithm for heart explants. A process diagram of the application created within the software Proceedix is shown. Each discrete process of grossing is represented by a separate blue box. Each process requires documentation of observations and/or measurements. Navigation of the application is performed by voice commands. In brief, the green circle indicates the start process, which is followed by preparation of cassettes, measuring the heart weight, and evaluation of the coronary arteries. A decision tree is shown with the red ovals which is guided by the absence or presence of coronary artery disease (CAD). The process continues until the last process, which is the listing of the cassettes and then exiting the program (orange circle).

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The software programmer trained the 3 users of the device for 25 to 30 minutes each. The users were pathology residents with 8 to 12 months of surgical pathology experience. As part of their residency training, all users had prior experience grossing heart and kidney specimens.

Microsoft Excel Data Analysis ToolPak was used for analysis. Data are presented as mean ± SD. A student t-test was used for paired data, while ANOVA was used for comparison among 3 groups. A P value of < .05 was defined as significant.

The heart grossing times (mean ± SD) were 17.57 (±1.43), 17.17 (±1.13), and 18.59 minutes (±2.32) for each of the 3 users, respectively. Differences among users were not overall significant (P > .99) (Figure 3A). When heart grossing times were organized by the first, second, and third grossing attempt, the average times per attempt were 23.23 (±1), 15.93 (±0.4), and 14.17 minutes (±0.22) for the 3 users, respectively. Overall differences between attempts were significant (P = .002) (Figure 3B). The first to last performance of the protocol led to a significant reduction in grossing time for each user. In addition to measuring the total time of the protocol performance, each substep within the protocol was automatically recorded. The step that took the longest time for each user was the measurement of the valvular lengths and wall thicknesses; the average times of the 3 users were 4.37 (±0.43), 5.70 (±0.52), 4.78 minutes (±0.93), respectively. The difference between the first and second users was significant (P = .03); the difference between the first and third users (P = .52) and the difference between the second and third users was not significant (P = .21).

Figure 3

Comparisons of heart grossing time. The average heart grossing time of 3 specimens for each user across 3 attempts were 17.57, 17.17, and 18.59 minutes, respectively (A). The average grossing time for 3 attempts showed no significant differences among users (P = .91, P = .84, and P = .77) or overall, by one-way ANOVA (F2,6 = .001; P > .99). The consecutive attempts were also examined across all 3 users (B). When the times were averaged by attempt, rather than by user, the average time per attempt was 23.23, 15.93, and 14.17 minutes, respectively. The difference between the first and each subsequent repeated attempt was significant (P = .02, P = .01, and P = .09). One-way ANOVA result was significant overall (F2,6 = 23.07; P = .002). *P is < .05.

Figure 4. Comparisons of kidney grossing time. The average kidney grossing time of 3 specimens for each user was 8.41, 8.70, and 14.65 minutes, respectively (A). The average grossing time for 3 specimens was significantly different between the first and third (P = .03), and the second and third (P = .04); however, there was no significant difference between the first and second users (P = .88); overall the one-way ANOVA result was significant (F2,6 = 6.626; P = .03). The consecutive attempts were also examined across all 3 users and were not significantly different (B). The times by attempt, rather than by user, were 12.27, 11.01, and 8.48 minutes, respectively (P = .70, P = .34, and P = .39). The one-way ANOVA result was (F2,6 = .783; P = .499). *P is < .05.

Figure 3

Comparisons of heart grossing time. The average heart grossing time of 3 specimens for each user across 3 attempts were 17.57, 17.17, and 18.59 minutes, respectively (A). The average grossing time for 3 attempts showed no significant differences among users (P = .91, P = .84, and P = .77) or overall, by one-way ANOVA (F2,6 = .001; P > .99). The consecutive attempts were also examined across all 3 users (B). When the times were averaged by attempt, rather than by user, the average time per attempt was 23.23, 15.93, and 14.17 minutes, respectively. The difference between the first and each subsequent repeated attempt was significant (P = .02, P = .01, and P = .09). One-way ANOVA result was significant overall (F2,6 = 23.07; P = .002). *P is < .05.

Figure 4. Comparisons of kidney grossing time. The average kidney grossing time of 3 specimens for each user was 8.41, 8.70, and 14.65 minutes, respectively (A). The average grossing time for 3 specimens was significantly different between the first and third (P = .03), and the second and third (P = .04); however, there was no significant difference between the first and second users (P = .88); overall the one-way ANOVA result was significant (F2,6 = 6.626; P = .03). The consecutive attempts were also examined across all 3 users and were not significantly different (B). The times by attempt, rather than by user, were 12.27, 11.01, and 8.48 minutes, respectively (P = .70, P = .34, and P = .39). The one-way ANOVA result was (F2,6 = .783; P = .499). *P is < .05.

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The kidney grossing times averaged 8.41 (±0.72), 8.70 (±0.81), and 14.65 minutes (±0.84) for each of the 3 users, respectively. The kidney grossing times for 3 specimens were significantly different among users (P = .03) (Figure 4A). When kidney grossing times were organized by the first, second, and third grossing attempt, the average times per attempt were 12.27 (±4.76), 11.01 (±2.43), and 8.48 minutes (±3.79) for the 3 users, respectively. Differences among the attempts were not significant (P = .499) (Figure 4B). Each substep within the protocol was automatically recorded along with the total time of the protocol. The step that took the longest time for each user was the evaluation and description of the cut surface; the average times of the 3 users were 3.03 (±0.68), 4.20 (±1.67), 4.87 minutes (±1.45), respectively. Differences between the first and second users, the first and third users, and second and third users the users were not significant (P = .33, P = .12, P = .63, respectively).

In this study, the advantages of process tracking with smart glass technology was demonstrated. For the heart specimen, there were no significant difference in grossing times among the 3 users when each user's overall time for 3 attempts was calculated. However, when data were analyzed by comparing user times based on the attempt, a significant difference was observed with each repeat performance. For the less complex kidney specimen, a significant difference was seen among the overall grossing times when each user's multiple attempts were combined. However, unlike the heart specimen, no significant improvement in time was observed with each subsequent performance.

This proof-of-concept study demonstrates a potential application for smart glasses in the pathology laboratory. Smart glasses provide a hands-free platform for data retrieval and recording. An additional benefit of smart glasses is the passive gathering of information for workflow management. Workflow timing is useful to document the relative efficiency of each user's performance. Smart glasses may be used to monitor resident skills and progress without direct oversight. A future application of monitoring resident training includes recording the grossing of surgical specimens with either constant supervision or recording for later review by a senior resident, fellow, or attending pathologist to evaluate the adequacy of grossing. It can also provide an attending pathologist with a first-site view of the surgical specimen and how it was sampled. These first-hand perspectives can be particularly useful for complex/unusual specimens. Another example could be facilitating resident fine-needle aspiration training, where a resident is performing a procedure under remote supervision. With all these training examples, the documentation of procedures can be automated and used to benchmark the efficiency and competency of a trainee. The time and specific steps taken by a trainee are recorded and the efficiency of repetitive procedures can be trended over time (e.g., grossing of routine specimens, such as placentas or biopsies).

In anatomic pathology, specimen worksheets or grossing templates are routinely used to describe features, such as the weight, orientation, surgical margin inking, tumor size, and tumor location.1  The key data fields from existing worksheets and grossing templates can be used to design smart glass protocols. In addition to traditional data, such as weight and measures, photography and video can be incorporated and archived. These records can be used in quality assurance programs to document adequacy of grossing as well as in the investigation of errors.

Additional features of current smart glasses include continuous photography, video recording, streaming video, teleconferences, and general data transmission.2  Google Glass is a device that has been demonstrated in concept for general medical practice,3  surgery,4,5  ophthalmology,6  emergency medicine,7,8  cardiology,9  and primary care.10  The applications in medical education have been demonstrated in the surgery, urology, and cardiology training programs. For example, augmented reality in surgical training labs has been used for teaching purposes; the device enables users to anticipate the procedures they perform by having each step projected into their field of vision.11  There is also a study using smart glasses by cardiology residents and fellows during training of cardiac procedures.

Smart glasses can be adapted in anatomic and clinical pathology to be used in education, training, evaluation of efficiency, and monitoring of laboratory operations. There may also be applications outside of the laboratory where still images and movies of organ grossing are viewed by surgeons or patients. For laboratory operations and efficiency there are case-use examples of smart glasses being used in factories to time specific actions of users as well as to document via movies and still-pictures. The process of performance tracking can be applied in surgical pathology by monitoring the grossing efficiency of pathologists, pathology assistants, and residents in terms of handling specimens and entering data into template forms. For training, the device can provide trainees hands-free access to grossing manuals, still-pictures, and videos illustrating the required way to perform a procedure. Furthermore, the device provides a hands-free platform for data entry. For education the hands-free devices may assist in documentation of cases in both still photographs and video. Finally, in addition to providing hands-free data entry and access to information, smart glasses have the additional benefit of augmented reality. In augmented reality, the computer-generated images are superimposed over the user's visualized environment. We envision that augmented reality platforms for surgical pathology will entail overlaying a virtual map with grossing instructions superimposed on the physical specimen in the user's field of view. We anticipate wearable technologies, such as smart glasses, have a role in the practice of pathology.

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Author notes

JY Park is on the scientific advisory board of Miraca Holdings; Baylor Genetics, Fujirebio and SRL Labs are subsidiaries of Miraca Holdings. The other authors have no relevant financial interest in the products or companies described in this article.