Tool to visualize and evaluate operator proficiency in laser hair-removal treatments

BACKGROUND: The uniform delivery of laser energy is particularly important for safe and effective laser hair removal (LHR) treatment. Although it is necessary to quantitatively assess the spatial distribution of the delivered laser, laser spots are difficult to trace owing to a lack of visual cues. This study proposes a novel preclinic tool to evaluate operator proficiency in LHR treatment and applies this tool to train novice operators and compare two different treatment techniques (sliding versus spot-by-spot). METHODS: A simulation bed is constructed to visualize the irradiated laser spots. Six novice operators are recruited to perform four sessions of simulation while changing the treatment techniques and the presence of feedback (sliding without feedback, sliding with feedback, spot-by-spot without feedback, and spot-by-spot with feedback). Laser distribution maps (LDMs) are reconstructed through a series of images processed from the recorded video for each simulation session. Then, an experienced dermatologist classifies the collected LDMs into three different performance groups, which are quantitatively analyzed in terms of four performance indices. RESULTS: The performance groups are characterized by using a combination of four proposed indices. The best-performing group exhibited the lowest amount of randomness in laser delivery and accurate estimation of mean spot distances. The training was only effective in the sliding treatment technique. After the training, omission errors decreased by 6.32% and better estimation of the mean spot distance of the actual size of the laser-emitting window was achieved. Gels required operators to be trained when the spot-by-spot technique was used, and imposed difficulties in maintaining regular laser delivery when the sliding technique was used. CONCLUSIONS: Because the proposed system is simple and highly affordable, it is expected to benefit many operators in clinics to train and maintain skilled performance in LHR treatment, which will eventually lead to accomplishing a uniform laser delivery for safe and effective LHR treatment

Kinect shoulder data

<p>This data is numerical which measured shoulder range of motions by Kinect or goniometer. </p> <p>It is part of data which we examined. </p

Tool to visualize and evaluate operator proficiency in laser hair-removal treatments

Background The uniform delivery of laser energy is particularly important for safe and effective laser hair removal (LHR) treatment. Although it is necessary to quantitatively assess the spatial distribution of the delivered laser, laser spots are difficult to trace owing to a lack of visual cues. This study proposes a novel preclinic tool to evaluate operator proficiency in LHR treatment and applies this tool to train novice operators and compare two different treatment techniques (sliding versus spot-by-spot). Methods A simulation bed is constructed to visualize the irradiated laser spots. Six novice operators are recruited to perform four sessions of simulation while changing the treatment techniques and the presence of feedback (sliding without feedback, sliding with feedback, spot-by-spot without feedback, and spot-by-spot with feedback). Laser distribution maps (LDMs) are reconstructed through a series of images processed from the recorded video for each simulation session. Then, an experienced dermatologist classifies the collected LDMs into three different performance groups, which are quantitatively analyzed in terms of four performance indices. Results The performance groups are characterized by using a combination of four proposed indices. The best-performing group exhibited the lowest amount of randomness in laser delivery and accurate estimation of mean spot distances. The training was only effective in the sliding treatment technique. After the training, omission errors decreased by 6.32% and better estimation of the mean spot distance of the actual size of the laser-emitting window was achieved. Gels required operators to be trained when the spot-by-spot technique was used, and imposed difficulties in maintaining regular laser delivery when the sliding technique was used. Conclusions Because the proposed system is simple and highly affordable, it is expected to benefit many operators in clinics to train and maintain skilled performance in LHR treatment, which will eventually lead to accomplishing a uniform laser delivery for safe and effective LHR treatment

Measurement of Shoulder Range of Motion in Patients with Adhesive Capsulitis Using a Kinect.

Range of motion (ROM) measurements are essential for the evaluation for and diagnosis of adhesive capsulitis of the shoulder (AC). However, taking these measurements using a goniometer is inconvenient and sometimes unreliable. The Kinect (Microsoft, Seattle, WA, USA) is gaining attention as a new motion detecting device that is nonintrusive and easy to implement. This study aimed to apply Kinect to measure shoulder ROM in AC; we evaluated its validity by calculating the agreement of the measurements obtained using Kinect with those obtained using goniometer and assessed its utility for the diagnosis of AC. Both shoulders of 15 healthy volunteers and affected shoulders of 12 patients with AC were included in the study. The passive and active ROM of each were measured with a goniometer for flexion, abduction, and external rotation. Their active shoulder motions for each direction were again captured using Kinect and the ROM values were calculated. The agreement between the two measurements was tested with the intraclass correlation coefficient (ICC). Diagnostic performance using the Kinect ROM was evaluated with Cohen's kappa value. The cutoff values of the limited ROM were determined in the following ways: the same as passive ROM values, reflecting the mean difference, and based on receiver operating characteristic curves. The ICC for flexion/abduction/external rotation between goniometric passive ROM and the Kinect ROM were 0.906/0.942/0.911, while those between active ROMs and the Kinect ROMs were 0.864/0.932/0.925. Cohen's kappa values were 0.88, 0.88, and 1.0 with the cutoff values in the order above. Measurements of the shoulder ROM using Kinect show excellent agreement with those taken using a goniometer. These results indicate that the Kinect can be used to measure shoulder ROM and to diagnose AC as an alternative to goniometer

Pneumatic-type surgical robot end-effector for laparoscopic surgical-operation-by-wire

Background Although minimally invasive surgery (MIS) affords several advantages compared to conventional open surgery, robotic MIS systems still have many limitations. One of the limitations is the non-uniform gripping force due to mechanical strings of the existing systems. To overcome this limitation, a surgical instrument with a pneumatic gripping system consisting of a compressor, catheter balloon, micro motor, and other parts is developed. Method This study aims to implement a surgical instrument with a pneumatic gripping system and pitching/yawing joints using micro motors and without mechanical strings based on the surgical-operation-by-wire (SOBW) concept. A 6-axis external arm for increasing degrees of freedom (DOFs) is integrated with the surgical instrument using LabVIEW® for laparoscopic procedures. The gripping force is measured over a wide range of pressures and compared with the simulated ideal step function. Furthermore, a kinematic analysis is conducted. To validate and evaluate the systems clinical applicability, a simple peg task experiment and workspace identification experiment are performed with five novice volunteers using the fundamentals of laparoscopic surgery (FLS) board kit. The master interface of the proposed system employs the hands-on-throttle-and-stick (HOTAS) controller used in aerospace engineering. To develop an improved HOTAS (iHOTAS) controller, 6-axis force/torque sensor was integrated in the special housing. Results The mean gripping force (after 1,000 repetitions) at a pressure of 0.3 MPa was measured to be 5.8 N. The reaction time was found to be 0.4 s, which is almost real-time. All novice volunteers could complete the simple peg task within a mean time of 176 s, and none of them exceeded the 300 s cut-off time. The systems workspace was calculated to be 11,157.0 cm3. Conclusions The proposed pneumatic gripping system provides a force consistent with that of other robotic MIS systems. It provides near real-time control. It is more durable than the existing other surgical robot systems. Its workspace is sufficient for clinical surgery. Therefore, the proposed system is expected to be widely used for laparoscopic robotic surgery. This research using iHOTAS will be applied to the tactile force feedback system for surgeons safe operation

Measurement of the shoulder range of motion (ROM).

<p>A. Goniometric active ROM measurement of flexion, abduction, and external rotation by the examiner. B. The Kinect measurement of flexion, abduction, and external rotation ROM under instruction from the examiner. The individual in this figure has given written informed consent (as outlined in PLOS consent form) to publish these case details.</p

Diagnosis of adhesive capsulitis of the shoulder (AC) using shoulder range of motion measured with the Kinect (kROMs).

<p>A. Box plots for the comparison of kROMs between healthy controls (HCs) (N = 30) and AC patients (N = 12) for flexion, abduction, and external rotation, respectively. B. Receiver operating characteristic curves for the diagnosis of AC using kROMs in each direction. Areas under the curves are shown.</p

Subjects’ clinical characteristics.

<p>HC, healthy controls; AC, adhesive capsulitis; SD, standard deviation</p><p>Subjects’ clinical characteristics.</p

Measurement and calculation of the shoulder range of motions (ROMs) using the Kinect.

<p>A. Red, green, and blue image, depth image, and converted skeleton image from the Kinect. B. Traces of body segments during each shoulder motion. The green line is the trace of the left wrist, while the pink line is the trace of the left elbow. C. Calculation of the shoulder ROM angles by the projected angles on the defined anatomical planes. The individual in this figure has given written informed consent (as outlined in PLOS consent form) to publish these case details.</p

Bland-Altman plots of kROMs, pROMs, and aROMs.

<p>The circles show 30 shoulders of 15 healthy controls, while the triangles show 12 affected shoulders of patients with adhesive capsulitis (42 shoulders total). Mean differences are indicated by the solid line and 95% limits of agreement (mean differences ± 1.96 standard deviation of the difference) are shown by the dashed line. The dotted line shows regression lines for proportional biases. A. Comparison between kROMs and aROMs. B. Comparison between kROMs and pROMs. C. Comparison between aROMs and pROMs. kROMs, shoulder range of motion measured with the Kinect; pROMs, passive shoulder range of motion measured with the goniometer; aROMs, active shoulder range of motion measured with the goniometer.</p