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Learning to use a surgical microscope is a fundamental step in otolaryngology training; however, there is currently no objective method to teach or assess this skill. Tympanostomy tube placement is a common otologic procedure that requires skilled use of a surgical microscope. This study was designed to (1) implement metrics capable of evaluating microscope use and (2) establish construct validity. This was a prospective cohort study. Eight otolaryngology trainees and three otolaryngology experts were asked to use a microscope to insert a tympanostomy tube into a cadaveric myringotomy in a standardized setting. Microscope movements were tracked in a three-dimensional space, and tracking metrics were applied to the data. The procedure was video-recorded and then analyzed by blinded experts using operational metrics. Results from both groups were compared, and discriminatory metrics were determined. The following tracking metrics were identified as discriminatory between the trainee and expert groups: total completion time, operation time, still time, and jitter (movement perturbation). Many operational metrics were found to be discriminatory between the two groups, including several positioning metrics, optical metrics, and procedural metrics. Performance metrics were implemented, and construct validity was established for a subset of the proposed metrics by discriminating between expert and novice participants. These discriminatory metrics could form the basis of an automated system for providing feedback to residents during training while using a myringotomy surgical simulator. Additionally, these metrics may be useful in guiding a standardized teaching and evaluation methodology for training in the use of surgical microscopes.

Intraoperative tumor localization in a deflated lung in minimally invasive surgery (MIS) is challenging as the lung cannot be manually palpated through small incisions. To do so remotely, an articulated multisensory imaging device combining tactile and ultrasound sensors was developed. It visualizes the surface tactile map and the depth of the tissue. However, with little maneuverability in MIS, localizing tumors using instrumented palpation is both tedious and inefficient. In this paper, a texture- based image guidance system that classifies tactile-guided ultrasound texture regions and provides beliefs on their types is proposed. The resulting interactive feedback allows directed palpation in MIS. A k-means classifier is used to first cluster gray-level co-occurrence matrix (GLCM)-based texture features of the ultrasound regions, followed by hidden Markov model-based belief propagation to establish confidence about the clustered features observing repeated patterns. When the beliefs converge, the system autonomously detects tumor and nontumor textures. The approach was tested on 20 ex vivo soft tissue specimens in a staged MIS. The results showed that with guidance, tumors in MIS could be localized with 98% accuracy, 99% sensitivity, and 97% specificity.Clinical Relevance- Texture-based image guidance adds efficiency and control to instrumented palpation in MIS. It renders fluidity and accuracy in image acquisition using a hand-held device where fatigue from prolonged handling affects imaging quality.

Learning to use a surgical microscope is a fundamental step in otolaryngology training; however, there is currently no objective method to teach or assess this skill. Tympanostomy tube placement is a common otologic procedure that requires skilled use of a surgical microscope. This study was designed to (1) implement metrics capable of evaluating microscope use and (2) establish construct validity.This was a prospective cohort study.Eight otolaryngology trainees and three otolaryngology experts were asked to use a microscope to insert a tympanostomy tube into a cadaveric myringotomy in a standardized setting. Microscope movements were tracked in a three-dimensional space, and tracking metrics were applied to the data. The procedure was video-recorded and then analyzed by blinded experts using operational metrics. Results from both groups were compared, and discriminatory metrics were determined.The following tracking metrics were identified as discriminatory between the trainee and expert groups: total completion time, operation time, still time, and jitter (movement perturbation). Many operational metrics were found to be discriminatory between the two groups, including several positioning metrics, optical metrics, and procedural metrics.Performance metrics were implemented, and construct validity was established for a subset of the proposed metrics by discriminating between expert and novice participants. These discriminatory metrics could form the basis of an automated system for providing feedback to residents during training while using a myringotomy surgical simulator. Additionally, these metrics may be useful in guiding a standardized teaching and evaluation methodology for training in the use of surgical microscopes.

Conventional surgical methods are effective for treating lung tumors; however, they impose high trauma and pain to patients. Minimally invasive surgery is a safer alternative as smaller incisions are required to reach the lung; however, it is challenging due to inadequate intraoperative tumor localization. To address this issue, a mechatronic palpation device was developed that incorporates tactile and ultrasound sensors capable of acquiring surface and cross-sectional images of palpated tissue. Initial work focused on tactile image segmentation and fusion of position-tracked tactile images, resulting in a reconstruction of the palpated surface to compute the spatial locations of underlying tumors. This paper presents a computational model capable of analyzing orthogonally-paired tactile and ultrasound images to compute the surface circumference and depth margins of a tumor. The framework also integrates an error compensation technique and an algebraic model to align all of the image pairs and to estimate the tumor depths within the tracked thickness of a palpated tissue. For validation, an ex vivo experimental study was conducted involving the complete palpation of 11 porcine liver tissues injected with iodine-agar tumors of varying sizes and shapes. The resulting tactile and ultrasound images were then processed using the proposed model to compute the tumor margins and compare them to fluoroscopy based physical measurements. The results show a good negative correlation ( r = −0.783, p = 0.004) between the tumor surface margins and a good positive correlation ( r = 0.743, p = 0.009) between the tumor depth margins.

Tumor localization in traditional lung resection surgery requires manual palpation of the deflated lung through a thoracotomy. It is a painful procedure that is not suitable for many patients. Therefore, a multisensory mechatronic device was designed to localize tumors using a minimally invasive approach. The device is sensorized with tactile, ultrasound and position sensors in order to obtain multimodal data of soft tissue in real time. This paper presents the validation of the efficiency and efficacy of this device via an ex vivo experimental study. Tumor pathology was simulated by embedding iodine-agar phantom tumors of varying shapes and sizes into porcine liver tissue. The device was then used to palpate the tissue to localize and visualize the simulated tumors. Markers were then placed on the location of the tumors and fluoroscopic imaging was performed on the tissue in order to determine the localization accuracy of the device. Our results show that the device localized 87.5% of the tumors with an average deviation from the tumor center of 3.42 mm.

Integrating learning styles in adaptive educational systems are a growing trend in technology enhanced learning. Children have different learning styles, abilities, preferences that focus on different types of information and process new information in different ways. Providing adaptively based on learning styles can promote interest for learners and make learning easier for them. The purpose of our research is to adopt an e-Learning approach Radio Frequency Identification (RFID) technology in order to model the Visual, Auditory and Kinesthetic (VAK) learning style focused on Learning Disabilities (LD) children. Today’s technology offers great chances to assist students with disabilities to live freely and learn more easily. Developing the learning environments assisted by technology is a new way in making their learning processes successful.

This paper describes a method to measure the rocking motion of lab-scale armour units. Sensors as found in mobile phones are used. These sensors, data-storage and battery are all embedded in the model units, such that they can be applied without wires attached to them. The technique is applied to double-layer units in order to compare the results to the existing knowledge for this type of armour layers. In contrast to previous research, the gyroscope reading is used to determine the (rocking) impact velocities. Two pioneer measurement series are described. From the readings both the temporal distribution of rocking can be inferred, as well as the spatial distribution. The temporal probability distribution for the rocking events seems logarithmic, with the impact velocity u2% being in the same order of magnitude as those reported earlier. These measurements indicate that for a randomly placed cube in an armour layer most rocking and most violent impact velocities occur about 2Dn under the waterline, instead of around the waterline. Moreover, the wave steepness is seen to have an effect on the rocking intensity. From the measurements with multiple units it can be seen that the measured impact velocity exhibits a large spatial variation among different units at an otherwise equal location.