Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV. Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective. Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education.
In order to prevent dislocation of the hip after total hip arthroplasty
(THA), patients have to adhere to precautions in the early post-operative
period. The hypothesis of this study was that a protocol with minimal
precautions after primary THA using the posterolateral approach
would not increase the short-term (less than three months) risk
of dislocation. We prospectively monitored a group of unselected patients undergoing
primary THA managed with standard precautions (n = 109, median age
68.9 years; interquartile range (IQR) 61.2 to 77.3) and a group
who were managed with fewer precautions (n = 108, median age 67.2
years; IQR 59.8 to 73.2). There were no significant differences between
the groups in relation to predisposing risk factors. The diameter
of the femoral head ranged from 28 mm to 36 mm; meticulous soft-tissue
repair was undertaken in all patients. The medical records were
reviewed and all patients were contacted three months post-operatively
to confirm whether they had experienced a dislocation. Aims
Patients and Methods
