Cingal provides immediate and long-term relief of osteoarthritis-related pain, stiffness, and function, significant through 26 weeks compared to saline. Cingal had similar immediate advantages compared with HA alone, while showing benefit comparable to HA at 6 weeks and beyond.

The vulnerability of gamma irradiation of TA was less than Achilles and quadriceps tendons.

Objectives: The aim of this study was to analyze the postoperative effects of extended nerve blocks and local infiltration analgesia (LIA) on postoperative pain control, muscle weakness, and blood loss after total knee arthroplasty (TKA). Patients and methods: Between February 24th 2020 and July 10th 2020, a total of 161 patients (55 males, 106 females; median age: 69.0 years [IQR 63.0-75.0], range, 41 to 81 years) who underwent primary TKA were randomly allocated into three parallel groups according to their concomitant procedure in a double-blind fashion: (i) those to whom nerve blockade was performed after competition of surgery under the duration of spinal anesthesia (n=50); (ii) those to whom LIA was performed during surgery (n=52), and (iii) control group (n=59). The content of LIA was 10-10 mL of 20 mg lidocaine with 0.01 mg adrenalin and 100 mg ropivacaine, 1 mL (30 mg) ketorolac, and 5 mL (500 mg) tranexamic acid was diluted by 50 mL 0.9% NaCl under aseptic conditions. Outcome parameters were the evaluation of pain until the evening of first postoperative day (24 to 36 h), mobilization, and blood loss within the first three postoperative days. Results: The pain was maximal between 4 and 8 h postoperatively, when the effect of the spinal anesthetic drugs disappeared. During this critical period, tolerable pain (Numerical Rating Scale, NRS ≤3) was observed in 52%, 42%, and 19% of nerve blockade in LIA and control groups, respectively. None of the patients complained of high-intensity pain (NRS >8) in the LIA group, which was a significant difference from the block and control groups (10% and 14%, p<0.008, respectively). There was no significant muscle weakness associated with the use of this extended block. The decrease in hemoglobin level was significantly lower in the LIA group than in the control and block groups (odds ratio [OR]: 0.379, 95% confidence interval [CI]: 0.165-0.874 for nerve blockade vs. LIA, OR: 1.189, 95% CI: 0.491-2.880 for nerve blockade vs. control, OR: 0.319, 95% CI: 0.140-0.727, respectively). The common language effect size for pain in each referred interval in each group and for decrease of hemoglobin between the first and third postoperative days fell between 0.507 and 0.680. Conclusion: This study demonstrates that LIA technique offers a fast and safe treatment option for pain relief after TKA. No clinically relevant muscle weakness was observed among groups according to field block applications. Significant advantages were also achieved in blood loss.

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Összefoglaló. Bevezetés: A térdízületnek ultrafriss osteochondralis allograft segítségével történő részleges ortopédiai rekonstrukciója képalkotó vizsgálatokon alapuló pontos tervezést igényel, mely folyamatban a morfológia felismerésére képes mesterséges intelligencia nagy segítséget jelenthet. Célkitűzés: Jelen kutatásunk célja a porc morfológiájának MR-felvételen történő felismerésére alkalmas mesterséges intelligencia kifejlesztése volt. Módszer: A feladatra legalkalmasabb MR-szekvencia meghatározása és 180 térd-MR-felvétel elkészítése után a mesterséges intelligencia tanításához manuálisan és félautomata szegmentálási módszerrel bejelölt porckontúrokkal tréninghalmazt hoztunk létre. A mély convolutiós neuralis hálózaton alapuló mesterséges intelligenciát ezekkel az adatokkal tanítottuk be. Eredmények: Munkánk eredménye, hogy a mesterséges intelligencia képes a meghatározott szekvenciájú MR-felvételen a porcnak a műtéti tervezéshez szükséges pontosságú bejelölésére, mely az első lépés a gép által végzett műtéti tervezés felé. Következtetés: A választott technológia – a mesterséges intelligencia – alkalmasnak tűnik a porc geometriájával kapcsolatos feladatok megoldására, ami széles körű alkalmazási lehetőséget teremt az ízületi terápiában. Orv Hetil. 2021; 162(9): 352–360. Summary. Introduction: The partial orthopedic reconstruction of the knee joint with an osteochondral allograft requires precise planning based on medical imaging reliant; an artificial intelligence capable of determining the morphology of the cartilage tissue can be of great help in such a planning. Objective: We aimed to develop and train an artificial intelligence capable of determining the cartilage morphology in a knee joint based on an MR image. Method: After having determined the most appropriate MR sequence to use for this project and having acquired 180 knee MR images, we created the training set for the artificial intelligence by manually and semi-automatically segmenting the contours of the cartilage in the images. We then trained the neural network with this dataset. Results: As a result of our work, the artificial intelligence is capable to determine the morphology of the cartilage tissue in the MR image to a level of accuracy that is sufficient for surgery planning, therefore we have made the first step towards machine-planned surgeries. Conclusion: The selected technology – artificial intelligence – seems capable of solving tasks related to cartilage geometry, creating a wide range of application opportunities in joint therapy. Orv Hetil. 2021; 162(9): 352–360.