Fiberbots: Robotic fibers for high-precision minimally invasive surgery.

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

An Automatized Deep Segmentation and Classification Model for Lumbar Disk Degeneration and Clarification of Its Impact on Clinical Decisions.

STUDY DESIGN: Cross-sectional database study. OBJECTIVE: The purpose of this study was to develop a successful, reproducible, and reliable convolutional neural network (CNN) model capable of segmentation and classification for grading intervertebral disc degeneration (IVDD), as well as quantify the network's impact on doctors' clinical decision-making. METHODS: 5685 discs from 1137 patients were graded separately by four experienced doctors according to the Pfirrmann classification. A ground truth (GT) was established for each disc in accordance with the decision of the majority of doctors. The U-net model is used for segmentation. 1815 discs from 363 patients were used to train and test the U-net. The Inception V3 model is employed for classification. All discs were separated into two distinct sets: 90% in a training set and 10% in a test set. The performance metrics of these models were measured. Reliability tests were performed. The impact of CNN assistance on doctors was assessed. RESULTS: Segmentation accuracy was .9597 with a .8717 Jaccard Index and a .9314 Sorensen Dice coefficient. Classification accuracy is .9346, and the F1 score is .9355. The intraclass correlation coefficient (ICC) and kappa values between CNN and GT were .95-.97. With CNN's assistance, the success rates of doctors increased by 7.9% to 22%. CONCLUSIONS: The fully automated network outperformed doctors markedly in terms of accuracy and reliability. The results of CNN were comparable to those of other recent studies in the literature. It was determined that CNN's assistance had a substantial positive effect on the doctor's decision.

On the application of hydrodynamic cavitation on a chip in cellular injury and drug delivery.

Hydrodynamic cavitation (HC) is a phase change phenomenon, where energy release in a fluid occurs upon the collapse of bubbles, which form due to the low local pressures. During recent years, due to advances in lab-on-a-chip technologies, HC-on-a-chip (HCOC) and its potential applications have attracted considerable interest. Microfluidic devices enable the performance of controlled experiments by enabling spatial control over the cavitation process and by precisely monitoring its evolution. In this study, we propose the adjunctive use of HC to induce distinct zones of cellular injury and enhance the anticancer efficacy of Doxorubicin (DOX). HC caused different regions (lysis, necrosis, permeabilization, and unaffected regions) upon exposure of different cancer and normal cells to HC. Moreover, HC was also applied to the confluent cell monolayer following the DOX treatment. Here, it was shown that the combination of DOX and HC exhibited a more pronounced anticancer activity on cancer cells than DOX alone. The effect of HC on cell permeabilization was also proven by using carbon dots (CDs). Finally, the cell stiffness parameter, which was associated with cell proliferation, migration and metastasis, was investigated with the use of cancer cells and normal cells under HC exposure. The HCOC offers the advantage of creating well-defined zones of bio-responses upon HC exposure simultaneously within minutes, achieving cell lysis and molecular delivery through permeabilization by providing spatial control. In conclusion, micro scale hydrodynamic cavitation proposes a promising alternative to be used to increase the therapeutic efficacy of anticancer drugs.

Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves' Features.

Treadmill Exercise Test (TET) results and patients' clinical symptoms influence cardiologists' decision to perform Coronary Angiography (CAG) which is an invasive procedure. Since TET has high false positive rates, it can cause an unnecessary invasive CAG. Our primary objective was to develop a machine learning model capable of optimizing TET performance based on electrocardiography (ECG) waves characteristics and signals. TET reports from 294 patients who underwent CAG following high risk TET were collected and categorized into those with critical CAD and others. The signal was converted to time series format. A dataset containing the P, QRS, and T wave times and amplitudes was created. Using this dataset, 5 machine learning algorithms were trained with 5-fold cross validation. All these models were then compared to the performance of cardiologists on V5 signal. The results from 5 machine learning models were clearly superior to the cardiologists' V5 signal performance (P < 0.0001). In addition, the XGBoost model, with an accuracy of 80.92±6.42% and an area under the curve (AUC) of 0.78±0.06, was the most successful model. Machine learning models can produce high-performance diagnoses using the V5 signal markers only as it does not require any clinical markers obtained from TET reports. This can lead to significant contributions to improving clinical prediction in non-invasive methods.

Acousto-holographic reconstruction of whole-cell stiffness maps.

Accurate assessment of cell stiffness distribution is essential due to the critical role of cell mechanobiology in regulation of vital cellular processes like proliferation, adhesion, migration, and motility. Stiffness provides critical information in understanding onset and progress of various diseases, including metastasis and differentiation of cancer. Atomic force microscopy and optical trapping set the gold standard in stiffness measurements. However, their widespread use has been hampered with long processing times, unreliable contact point determination, physical damage to cells, and unsuitability for multiple cell analysis. Here, we demonstrate a simple, fast, label-free, and high-resolution technique using acoustic stimulation and holographic imaging to reconstruct stiffness maps of single cells. We used this acousto-holographic method to determine stiffness maps of HCT116 and CTC-mimicking HCT116 cells and differentiate between them. Our system would enable widespread use of whole-cell stiffness measurements in clinical and research settings for cancer studies, disease modeling, drug testing, and diagnostics.

MobileSkin: Classification of Skin Lesion Images Acquired Using Mobile Phone-Attached Hand-Held Dermoscopes.

Dermoscopy is the visual examination of the skin under a polarized or non-polarized light source. By using dermoscopic equipment, many lesion patterns that are invisible under visible light can be clearly distinguished. Thus, more accurate decisions can be made regarding the treatment of skin lesions. The use of images collected from a dermoscope has both increased the performance of human examiners and allowed the development of deep learning models. The availability of large-scale dermoscopic datasets has allowed the development of deep learning models that can classify skin lesions with high accuracy. However, most dermoscopic datasets contain images that were collected from digital dermoscopic devices, as these devices are frequently used for clinical examination. However, dermatologists also often use non-digital hand-held (optomechanical) dermoscopes. This study presents a dataset consisting of dermoscopic images taken using a mobile phone-attached hand-held dermoscope. Four deep learning models based on the MobileNetV1, MobileNetV2, NASNetMobile, and Xception architectures have been developed to classify eight different lesion types using this dataset. The number of images in the dataset was increased with different data augmentation methods. The models were initialized with weights that were pre-trained on the ImageNet dataset, and then they were further fine-tuned using the presented dataset. The most successful models on the unseen test data, MobileNetV2 and Xception, had performances of 89.18% and 89.64%. The results were evaluated with the 5-fold cross-validation method and compared. Our method allows for automated examination of dermoscopic images taken with mobile phone-attached hand-held dermoscopes.

Deep convolutional neural networks for onychomycosis detection using microscopic images with KOH examination.

BACKGROUND: The diagnosis of superficial fungal infections is still mostly based on direct microscopic examination with potassium hydroxide solution. However, this method can be time consuming, and its diagnostic accuracy rates vary widely depending on the clinician's experience. OBJECTIVES: This study presents a deep neural network structure that enables the rapid solutions for these problems and can perform automatic fungi detection in grayscale images without dyes. METHODS: One hundred sixty microscopic full field photographs containing the fungal element, obtained from patients with onychomycosis, and 297 microscopic full field photographs containing dissolved keratin obtained from normal nails were collected. Smaller patches containing fungi (n = 1835) and keratin (n = 5238) were extracted from these full field images. In order to detect fungus and keratin, VGG16 and InceptionV3 models were developed by the use of these patches. The diagnostic performance of models was compared with 16 dermatologists by using 200 test patches. RESULTS: For the VGG16 model, the InceptionV3 model and 16 dermatologists, mean accuracy rates were 88.10 ± 0.8%, 88.78 ± 0.35% and 74.53 ± 8.57%, respectively; mean sensitivity rates were 75.04 ± 2.73%, 74.93 ± 4.52% and 74.81 ± 19.51%, respectively; and mean specificity rates were 92.67 ± 1.17%, 93.78 ± 1.74% and 74.25 ± 18.03%, respectively. The models were statistically superior to dermatologists according to rates of accuracy and specificity but not to sensitivity (p < .0001, p < .005 and p > .05, respectively). Area under curve values of the VGG16 and InceptionV3 models were 0.9339 and 0.9292, respectively. CONCLUSION: Our research demonstrates that it is possible to build an automated system capable of detecting fungi present in microscopic images employing the proposed deep learning models. It has great potential for fungal detection applications based on AI.

A Flexible Cystoscope Based on Hydrodynamic Cavitation for Tumor Tissue Ablation.

OBJECTIVE: Hydrodynamic cavitation is characterized by the formation of bubbles inside a flow due to local reduction of pressure below the saturation vapor pressure. The resulting growth and violent collapse of bubbles lead to a huge amount of released energy. This energy can be implemented in different fields such as heat transfer enhancement, wastewater treatment and chemical reactions. In this study, a cystoscope based on small scale hydrodynamic cavitation was designed and fabricated to exploit the destructive energy of cavitation bubbles for treatment of tumor tissues. The developed device is equipped with a control system, which regulates the movement of the cystoscope in different directions. According to our experiments, the fabricated cystoscope was able to locate the target and expose cavitating flow to the target continuously and accurately. The designed cavitation probe embedded into the cystoscope caused a significant damage to prostate cancer and bladder cancer tissues within less than 15 minutes. The results of our experiments showed that the cavitation probe could be easily coupled with endoscopic devices because of its small diameter. We successfully integrated a biomedical camera, a suction tube, tendon cables, and the cavitation probe into a 6.7 mm diameter cystoscope, which could be controlled smoothly and accurately via a control system. The developed device is considered as a mechanical ablation therapy, can be a solid alternative for minimally invasive tissue ablation methods such as radiofrequency (RF) and laser ablation, and could have lower side effects compared to ultrasound therapy and cryoablation.

Micro-UFO (Untethered Floating Object): A Highly Accurate Microrobot Manipulation Technique.

A new microrobot manipulation technique with high precision (nano level) positional accuracy to move in a liquid environment with diamagnetic levitation is presented. Untethered manipulation of microrobots by means of externally applied magnetic forces has been emerging as a promising field of research, particularly due to its potential for medical and biological applications. The purpose of the presented method is to eliminate friction force between the surface of the substrate and microrobot. In an effort to achieve high accuracy motion, required magnetic force for the levitation of the microrobot was determined by finite element method (FEM) simulations in COMSOL (version 5.3, COMSOL Inc., Stockholm, Sweden) and verified by experimental results. According to position of the lifter magnet, the levitation height of the microrobot in the liquid was found analytically, and compared with the experimental results head-to-head. The stable working range of the microrobot is between 30 µm to 330 µm, and it was confirmed in both simulations and experimental results. It can follow the given trajectory with high accuracy (<1 µm error avg.) at varied speeds and levitation heights. Due to the nano-level positioning accuracy, desired locomotion can be achieved in pre-specified trajectories (sinusoidal or circular). During its locomotion, phase difference between lifter magnet and carrier magnet has been observed, and relation with drag force effect has been discussed. Without using strong electromagnets or bulky permanent magnets, our manipulation approach can move the microrobot in three dimensions in a liquid environment.

Stabilization of Microrobot Motion Characteristics in Liquid Media.

Magnetically actuated microrobot in a liquid media is faced with the problem of head-tilting reaction caused by its hydrodynamic structure and its speed while moving horizontally. When the instance microrobot starts a lateral motion, the drag force acting on it increases. Thus, the microrobot is unable to move parallel to the surface due to the existence of drag force that cannot be neglected, particularly at high speeds such as >5 mm/s. The effect of it scales exponentially at different speeds and the head-tilting angle of the microrobot changes relative to the reference surface. To the best of our knowledge, there is no prior study on this problem, and no solution has been proposed so far. In this study, we developed and experimented with 3 control models to stabilize microrobot motion characteristics in liquid media to achieve accurate lateral locomotion. The microrobot moves in an untethered manner, and its localization is carried out by a neodymium magnet (grade N48) placed inside its polymer body. This permanent magnet is called a carrier-magnet. The fabricated microrobot is levitated diamagnetically using a pyrolytic graphite placed under it and an external permanent magnet, called a lifter-magnet (grade N48), aligned above it. The lifter-magnet is attached to a servo motor mechanism which can control carrier-magnet orientation along with roll and pitch axes. Controlling the angle of this servo motor, together with the lifter-magnet, allowed us to cope with the head-tilting reaction instantly. Based on the finite element method (FEM), analyses that were designed according to this experimental setup, the equations giving the relation of microrobot speed with servo motor angle along with the microrobot head-tilting angle with servo motor angle, were derived. The control inputs were obtained by COMSOL® (version 5.3, COMSOL Inc., Stockholm, Sweden). Using these derived equations, the rule-based model, laser model, and hybrid model techniques were proposed in this study to decrease the head-tilting angle. Motion control algorithms were applied in di-ionized water medium. According to the results for these 3 control strategies, at higher speeds (>5 mm/s) and 5 mm horizontal motion trajectory, the average head-tilting angle was reduced to 2.7° with the ruled-based model, 1.1° with the laser model, and 0.7° with the hybrid model.

Vision-based automated single-cell loading and supply system.

Automated continuous individual cell transfer is a critical step in single-cell applications using microfluidic devices. Cells must be aspirated gently from a buffer before transferring to operation zone so as not to artificially perturb their biostructures. Vision-based manipulation is a key technique for allowing nondestructive cell transportation. In this paper, we presented a design for an automated single-cell loading and supply system that can be integrated with complex microfluidic applications for examining or processing one cell at a time such as the current nuclear transplantation method. The aim of the system is to automatically transfer mammalian donor ( approximately 15 microm) or oocyte ( approximately 100 microm) cells one by one from a container to a polydimethylsiloxane (PDMS) microchannel and then transport them to other modules. The system consists of two main parts: a single-cell suction module, and a PDMS-based microfluidic chip controlled by an external pump. The desired number of vacuumed cells can be directed into the microfluidic chip and stored in a docking area. From the batch, they can be moved to next module by activating pneumatic pressure valves located on two sides of the chip. The entire mechanism is combined with monitoring systems that perform detection/tracking and control.