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A medical school is a tertiary educational institution, professional school, or forms a part of such an institution, that teaches medicine, and awards a professional degree for physicians. Such medical degrees include the Bachelor of Medicine, Bachelor of Surgery (MBBS, MBChB, MBBCh, BMBS), Master of Medicine (MM, MMed), Doctor of Medicine (MD), or Doctor of Osteopathic Medicine (DO). Many medical schools offer additional degrees, such as a Doctor of Philosophy (PhD), master's degree (MSc) or other post-secondary education. Medical schools can also carry out medical research and operate teaching hospitals. Around the world, criteria, structure, teaching methodology, and nature of medical programs offered at medical schools vary considerably. Medical schools are often highly competitive, using standardized entrance examinations, as well as grade point averages and leadership roles, to narrow the selection criteria for candidates. In most countries, the study of medicine is completed

Supernumerary Robotic Arm for Three-Handed Surgical Application: Behavioral Study and Design of Human-Machine Interface

Elahe Abdi

In surgical to industrial manipulation, the operator needs assistance for tasks requiring more than two hands. Teamwork may be the source of errors and inefficiency, especially if the assistant is a novice or unfamiliar with the main operator. The need for assistance may become problematic in case of lack of human resources e.g. in emergency surgical cases in the late hours of the night. Our objective is to improve the surgeon's autonomy and dexterity by a robotic arm under his own control. Although a number of robotic instrument holders have been developed, the best way to control such devices is still an open question. No behavioral study has been conducted on the best control strategy and human performance in three-handed tasks. We have selected the foot for commanding the third arm on the basis of a literature review. A series of experiments in virtual environments has been conducted to study the feasibility of this choice. The first experiment compares performance in the same task using two or three hands. Results show that three-handed manipulation is preferred to two-handed manipulation in demanding tasks. The second experiment investigated the type of tasks to be aimed in three-handed manipulation and the learning curve of users. Moving the hands and a foot simultaneously in opposite directions was perceived as difficult compared to a more active task with liberty in choosing the limbs coordination. Limbs were moved in parallel rather than serially. The performance improved within a few minutes of practice. Also, the sense of ownership improved constantly during the experiment. Two other experiments were aimed at handling the endoscope in laparoscopic surgery. Surgeons and medical students participated in these studies. Residents had a more positive approach towards foot usage and performed better compared to more experienced surgeons. This proves that the best training period for surgeons to use a foot controlled robotic arm is during their residency. A realistic virtual abdominal cavity has been developed for the last experiment. This had a positive influence on the participants' performance and emphasizes the importance of using a familiar context for training such a "three-handed surgery". Finally, two different foot interfaces were developed to investigate the most intuitive third arm commanding strategy. A robotic arm is hence controlled by the foot's translation or rotation in one interface (isotonic interface), and by force or torque in the other one (isometric interface). An experimental behavioral study was conducted to compare the two devices. Isometric rate control was preferred to isotonic position control due to the lower physical burden and higher movement accuracy of the robot. It was shown that the proposed device for isometric rate control could be used for intuitive control of four DoFs of a slave robotic arm. This thesis is the first step in a systematic investigation of a three-handed manipulation, two biological hands and a foot controlled robotic assistant. Findings suggest a high potential in using the foot to become more autonomous in surgery as well as other fields. Users can learn the control paradigm in a short period of time with little mental and physical burden. We expect the developed foot interfaces to be the basis of future development of more intuitive control interfaces. We believe that foot controlled robotic arms will be commonly used in various surgical as well as industrial applications.

EPFL2017

Optical Coherence Correlation Spectroscopy

Stéphane Broillet

Questions such as biomolecular interactions, kinetics of enzymes, DNA conformational changes, cellular structures properties (among others) are addressed by characterizing the biological function of molecules. This function depends on their structure and their dynamic properties, and are usually studied with optical labels. A widely used technique for that purpose is fluorescence correlation spectroscopy (FCS) that adopts fluorophores as reporters of dynamic processes. However, photobleaching limits the observation time on the one hand, and saturation at high excitation intensities imposes constraints for the study of fast molecular processes on the other hand. Gold nanoparticles (NPs) are labels of particular interest to overcome these drawbacks due to their photostability, low cytotoxicity and their availability with biocompatible surface coatings. In addition, NPs have found widespread application in life sciences and medicine, such as labels for optical imaging, carriers for drug and gene delivery, targets for cancer cell imaging and phototherapy, and contrast agent for magnetic resonance imaging (MRI). These applications all require methods to characterize their behaviour in a biological environment. Medical use of NPs usually requires intravenous injection. Once NPs are introduced into the blood stream they become exposed to biomolecules in the plasma that form a so-called protein corona. Obviously, knowledge about this interaction is needed for a safe medical application of NPs. The uptake of NPs by cells depends mainly on this protein corona. It is thus very important to understand the structural and dynamic properties of the protein corona at the molecular level to judge the fate of NPs in the human body prior to clinical studies. We introduce Optical Coherence Correlation Spectroscopy (OCCS) which exploits the backscattered light of NPs illuminated by a broadband light source. Due to the measurement of a scattering signal, OCCS gives access to long time scales that are hardly accessible with FCS experiments. The signal acquisition is similar to Fourier-domain optical coherence microscopy (FDOCM) with a dark-field contrast. We present the theory of OCCS based on auto-correlation analysis. Numerical simulations were used to estimate the performance of our new spectroscopy method and to build an analytical model for the auto-correlation function for measuring the diffusion coefficient and concentration of NPs in solution. An application to the study of the protein corona on NPs is presented. The hydrodynamic radius of superparamagnetic iron oxide nanoparticles (SPIONs) with adsorbed bovine serum albumin (BSA) is measured under physiological conditions using OCCS.We also aimed at improving the detection of these NPs in highly scattering environments, for instance inside a cell. Photothermal optical lock-in OCCS (poli-OCCS) is proposed, which exploits the absorption properties of gold NPs to generate a specific signal due to the photothermal effect. Poli-OCCS is introduced and first proof-of-principle measurements with gold NPs are presented.

EPFL2014

Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop

Adrien Raphaël Depeursinge

Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no exception. Via a learning framework, very good classification accuracy can be obtained, but several parameters need to be optimized. This article describes a practical framework for efficient distributed parameter optimization. The proposed solutions are applicable for many research groups with heterogeneous computing infrastructures and for various machine learning algorithms. These infrastructures can easily be connected via distributed computation frameworks. We use the Hadoop framework to run and distribute both grid and random search strategies for hyperparameter optimization and cross-validations on a cluster of 21 nodes composed of desktop computers and servers. We show that significant speedups of up to 364× compared to a serial execution can be achieved using our in-house Hadoop cluster by distributing the computation and automatically pruning the search space while still identifying the best-performing parameter combinations. To the best of our knowledge, this is the first article presenting practical results in detail for complex data analysis tasks on such a heterogeneous infrastructure together with a linked simulation framework that allows for computing resource planning. The results are directly applicable in many scenarios and allow implementing an efficient and effective strategy for medical (image) data analysis and related learning approaches.