AI technology for remote clinical assessment and monitoring.

OBJECTIVE: To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside. METHOD: This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal-Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician. RESULTS: A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal-Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9). CONCLUSION: These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data.

Thoracic Erector Spinae Plane (T-ESP) Block Together With Intertransverse Process (ITP) Block for Laparoscopic Abdominal Surgery: A Case Report.

Laparoscopy has become a milestone with reduced surgical stress and postoperative pain. Evidence promotes erector spinae block for laparoscopic abdominal surgery, in particular for cholecystectomy. The thoracic paravertebral space block is the administration of local anesthetic into a wedge-shaped space on the antero-lateral thoracic spine and provides abdominal analgesia. We hypothesized that a combination of two paravertebral by proxy blocks (erector spinae and intertransverse process (ITP)) with multi-dermatomeric coverage and visceral pain control, with evidence for intra- and postoperative analgesia in thoracic and abdominal surgeries, may be a surgical anesthesia option for laparoscopy. A 42-year-old patient with gastroesophageal reflux disease (GERD) was scheduled for a laparoscopic Nissen fundoplication. He was 173 cm in height and weighed 90 kg (BMI 30 kg.m-2) and was classified in the American Society of Anesthesiologists Physical Status Classification System (ASA-PS) as 2. He had a history of difficult airway and refused general anesthesia. With the patient's informed written consent, we performed a bilateral thoracic erector spinae plane (T-ESP)/ITP blocks at the T4-8 level. Surgery was performed with the patient spontaneously breathing under sedation without complications. Hence, the combination of ESP-ITP blocks was a good anesthesia option for the planned surgery without side effects and optimal postoperative pain control.

Combination of Pericapsular Nerve Group (PENG) and Sacral Erector Spinae Plane (S-ESP) Blocks for Hip Fracture Pain and Surgery: A Case Series.

A hip fracture is a serious injury with life-threatening complications, and its risk rises with increasing age. A hip fracture can be a very painful condition, and prompt surgical treatment is recommended to reduce pain and complications. Pain management is considered integral to the management of a broken hip. The choice between general and regional anesthesia in hip fracture surgery continues to be a topic of debate because risks are potentially associated with both approaches. Nerve blockades have proven to be effective in reducing acute pain after a hip fracture and in the perioperative period. For this reason, many regional techniques have been introduced, such as the lumbar plexus block, fascia iliac block, femoral nerve block, and recently, the pericapsular nerve group (PENG) block. Hip joint innervation is complex, not limited to the lumbar plexus but also depending on the sciatic nerve and branches of the sacral plexus (superior and inferior gluteal nerves and an articular branch from the quadratus femoris nerve). We hypothesized that a combination of two emerging regional anesthesia techniques, such as the PENG block and sacral erector spinae plane (S-ESP) block, could represent a good option to obtain pain control of the whole hip joint without opioid administration intraoperatively and postoperatively. Here, we report the cases of three frail patients with significant comorbidities who underwent hip fracture surgery (two cases of intramedullary nailing and one hemiarthroplasty), in which we preoperatively performed PENG and S-ESP blocks. We registered optimal intraoperative and postoperative pain control up to 48 hours after surgery without complications and without opioid administration, allowing the surgery to be performed with intravenous sedation or laryngeal mask general anesthesia. The surgeries were uneventful, and no complications were reported. This approach warrants further investigation in hip fracture surgery.

Neuraxial Anesthesia for an Open Low Anterior Rectal Resection: Tip the Scales in Patient's Favor.

We present the case of a successful application of combined spinal-epidural anesthesia for a geriatric patient undergoing open cancer surgery. The patient, affected by multiple comorbidities, was proposed for an open anterior rectal resection. The implementation of a tailored protocol, incorporating neuraxial techniques such as epidural and spinal anesthesia, facilitated optimal pain management and expedited postoperative recovery improving perioperative outcomes, and highlighting the potential benefits of such strategies in selected cases.

Experimental validation of state equations and dynamic route maps for phase change memristive devices.

Phase Change Memory (PCM) is an emerging technology exploiting the rapid and reversible phase transition of certain chalcogenides to realize nanoscale memory elements. PCM devices are being explored as non-volatile storage-class memory and as computing elements for in-memory and neuromorphic computing. It is well-known that PCM exhibits several characteristics of a memristive device. In this work, based on the essential physical attributes of PCM devices, we exploit the concept of Dynamic Route Map (DRM) to capture the complex physics underlying these devices to describe them as memristive devices defined by a state-dependent Ohm's law. The efficacy of the DRM has been proven by comparing numerical results with experimental data obtained on PCM devices.

Equilibrium Propagation for Memristor-Based Recurrent Neural Networks.

Among the recent innovative technologies, memristor (memory-resistor) has attracted researchers attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule that is gaining particular interest because of its simplicity and biological plausibility. The overall goal of this work is to provide a novel (theoretical) analog computing platform based on memristor devices and recurrent neural networks that exploits the memristor device physics to implement two variations of the backpropagation algorithm: recurrent backpropagation and equilibrium propagation. In the first learning technique, the use of memristor-based synaptic weights permits to propagate the error signals in the network by means of the nonlinear dynamics via an analog side network. This makes the processing non-digital and different from the current procedures. However, the necessity of a side analog network for the propagation of error derivatives makes this technique still highly biologically implausible. In order to solve this limitation, it is therefore proposed an alternative solution to the use of a side network by introducing a learning technique used for energy-based models: equilibrium propagation. Experimental results show that both approaches significantly outperform conventional architectures used for pattern reconstruction. Furthermore, due to the high suitability for VLSI implementation of the equilibrium propagation learning rule, additional results on the classification of the MNIST dataset are here reported.