Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Explication of the Construct of Shared Care and the Prevention of Pressure Ulcers in Home Health Care

The purpose of this investigation was to render a more complete understanding of subjective perceptions of pressure ulcers from the perspective of family dyads, and to study the effect of these subjective experiences on preventive behaviors and pressure ulcer outcomes. A naturalistic inquiry, combined with objective measures, was used. Twenty-one dyads participated in four in-depth interviews to explore how they mentally represented and responded to the risk of pressure ulcers. Through the process of concept development, a lay representation of pressure ulcers was developed. This process produced a new concept, identified as “shared care,” that explained how the dyads interaction influenced preventive behavior. Shared care consists of three elements: communication of symptoms, decisions about how to respond to symptoms, and appraisals of reciprocity. Two contrasting patterns of care were identified: shared and directed/discrepant. In the shared care group, 10 patients were at risk for pressure ulcers but only 4 developed ulcers. In this discrepant care group, 3 patients were at risk and 2 developed pressure ulcers. Shared care was a pattern of interaction used successfully by family members to prevent pressure ulcers in patients at risk

Risk factors and risk prediction models for early complications following total hip arthroplasty

Treatment of end-stage hip osteoarthritis was revolutionized in the 1960s with the newly invented low-friction total hip arthroplasty (THA). Since then, an increasing number of both primary and revision THAs have been performed annually, especially over the past two decades. To achieve better outcomes, orthopedic surgeons should carefully select optimal patients and appropriate methods and devices. Risk prediction models have been developed to inform the surgeon and patient more precisely about the expected outcomes of the surgery. The use of such a tool could engage patients more closely in the decision-making process and guide surgeons in avoiding unnecessary risk. The aims of this doctoral thesis were: 1) to determine the risk factors for revision due to dislocation after primary THA; 2) to determine the risk factors for revision due to periprosthetic joint infection (PJI) after primary THA; 3) to develop risk prediction models for assessing the risk of the most common adverse outcomes after primary THA, based on versatile registry data from Finland; and 4) to develop risk prediction models for early revisions and death, and to evaluate the predictive potential of various machine learning algorithms for complications following primary THA, based on the Nordic Arthroplasty Register Association (NARA) dataset. ,, We found that posterior approach, fracture diagnosis, and American Society of Anesthesiologists class III–IV were associated with an increased risk of revision for dislocation after primary THA. The use of a 36 mm femoral head size decreased the risk of revision for dislocation. For PJI, we identified several modifiable variables increasing and decreasing the risk of revision. Especially patients with a high body mass index may be at even higher risk of developing infection than previously reported. We also successfully developed preoperative risk prediction models for PJI, dislocation, periprosthetic fracture, and death after primary THA. Based on the NARA dataset, we were able to demonstrate that complex risk prediction methods are not required to achieve maximum predictive potential. Hence, simpler models can improve usability. All the developed models can easily be used in clinical practice to serve individual risk estimations for adverse outcomes.--- Pitkälle edenneen lonkan nivelrikon hoito mullistui, kun moderni lonkan tekonivelleikkaus yleistyi 60-luvulla. Lonkan tekonivelen ensi- ja uusintaleikkausten määrät ovat kasvaneet merkittävästi erityisesti kahden viimeisen vuosikymmenen aikana. Uusintaleikkausten välttämiseksi ortopedien tulisi huolellisesti valita ensileikkaukseen sopivat potilaat sekä parhaat mahdolliset leikkausmenetelmät ja komponentit. Viime aikoina onkin kehitetty riskilaskureita, jotta sekä kirurgien että potilaiden ymmärrys odotettavissa olevasta lopputuloksesta paranisi. Riskilaskureiden avulla potilaat voidaan ottaa paremmin mukaan yhteiseen päätöksentekoon. Tässä väitöskirjatutkimuksessa selvitettiin riskitekijöitä lonkan tekonivelleikkauksen jälkeisille uusintaleikkauksille. Erityishuomion kohteena olivat tekonivelen sijoiltaanmenot sekä infektiot. Lisäksi kehitimme riskilaskurimalleja ennustamaan potilaskohtaista riskiä tyypillisimmille komplikaatioille ja kuolemalle lonkan ensitekonivelleikkauksen jälkeen. Tämä väitöskirja perustuu uudistetun Suomen Endoproteesirekisterin ja Pohjoismaisen tekonivelrekisterin tietoihin. Tutkimuksessa havaittiin taka-avauksen, reisiluun kaulan murtumadiagnoosin ja anestesiariskiluokkien III-IV altistavan uusintaleikkaukselle tekonivelen sijoiltaanmenon vuoksi. Käytettäessä 36 mm:n halkaisijan omaavia nuppeja sijoiltaanmenoriski oli matala. Lisäksi tunnistimme useita muuttujia, jotka olivat yhteydessä tekonivelen infektoitumiseen. Erityisesti potilaat, joilla on korkea painoindeksi, saattavat olla alttiimpia tekonivelinfektiolle, kuin mitä aikaisemmin on raportoitu. Kehitimme myös onnistuneesti riskilaskurimallit ennustamaan riskiä tekonivelen uusintaleikkaukselle infektion, sijoiltaanmenon ja periproteettisen murtuman johdosta sekä kuolemalle lonkan ensitekonivelleikkauksen jälkeen. Tärkeä havainto riskilaskurimallien kehityksessä oli myös se, että yksinkertaisilla menetelmillä pystytään ennustamaan riskiä yhtä hyvin kuin monimutkaisilla menetelmillä. Kaikkia kehittämiämme malleja voi käyttää kliinisen päätöksenteon tukena arvioimaan potilaskohtaista riskiä leikkauksen jälkeiselle epäsuotuisalle päätetapahtumalle

Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration

Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40% of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA) 201